<article id='forest_change' class='forest_change source-article'>
  <h2 class='source_category_title'>Forest change</h2>
  <p class='source_category_description'>Forest change data measure tree cover loss, tree cover gain, or forest disturbance.</p>

  <ul class='sources'>

    <li id='canopy' class='source-item first hide'>
      <div class='source_header'>
        <strong class='source_title'>Tree Cover Canopy Density Settings</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
        </div>

        <div class='source_table'>
          <p>Drag the handle to adjust the minimum tree cover canopy (TCC) density for Hansen/UMD/Google/USGS/NASA tree cover and tree cover loss displayed in the figures and infographics. TCC density represents the estimated percent of a pixel that was covered by tree canopy in the year 2000, as determined from the analysis of satellite imagery. For the tree cover loss data, TCC density therefore corresponds to the density of tree cover <u>before</u> loss occurred.</p>
          <p>Adjustments to the minimum TCC density will only affect tree cover and tree cover loss. This feature does not pertain to Hansen/UMD/Google/USGS/NASA tree cover gain or to other GFW data layers or country profile statistics. Tree cover gain is displayed with a set minimum TCC density greater than 50%.</p>
          <p>This feature is also available for the Country Rankings and for the map visualization and analysis. The TCC density minimum selected in the Country Profiles will be applied to the country or subnational jurisdiction analysis and related map visualization if accessed through the Country Profiles. However, an adjustment to the TCC density minimum through the map settings will not affect the statistics within the Country Profiles. The Country Rankings will not reflect adjustments made in individual Country Profiles.</p>
        </div>
      </div>
    </li>

    <li id='umd_tree_loss_gain' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Tree cover loss</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(annual, 30m, global, Hansen/UMD/Google/USGS/NASA)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies areas of gross tree cover loss</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>0.00025 decimal degrees or approximately 27.8 x 27.8 meters at the equator</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global land area (excluding Antarctica and other Arctic islands)</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Source data</dt>
            <dd>Landsat 5 TM, <a href='http://landsat.usgs.gov/' target='_blank'>Landsat 7 ETM+, and Landsat 8 OLI</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Annual</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Date of content</dt>
            <dd>2001–2013</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Tree cover canopy density</dt>
            <dd>Varies according to selection (click the gear icon on the map to change the minimum tree cover canopy density threshold)</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Cautions</dt>
            <dd>
              <p>This data layer was updated in January 2015 to extend the tree cover loss analysis to 2013. The 2013 data update included new Landsat 8 data (launched in February 2013) as well as re-processed 2010-2012 data from Landsat TM and ETM+, which increased the amount of change that could be detected, resulting in some changes in calculated tree cover loss for 2011 (global increase of 6%) and 2012 (increase of 22%). Calculated tree cover loss for 2001-2010 remains unchanged. The integrated use of the original 2001-2012 (Version 1.0) data and the updated 2011–2013 data (Version 1.1) should be performed with caution.</p>

              <p>For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.</p>
              <p>When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).</p>
              <p>Read a summary of the accuracy of this data <a href='http://blog.globalforestwatch.org/2015/12/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1/'>here</a>.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the <a href='http://landsat.usgs.gov/science_L7_cpf.php' target='_blank'>Landsat 7 thematic mapper plus (ETM+)</a>, and <a href='http://landsat.usgs.gov/science_L7_cpf.php' target='_blank'>Landsat 7 thematic mapper plus (ETM+)</a>, and <a href='http://landsat.usgs.gov/landsat8.php' target='_blank'>Landsat 8</a> Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and approximately 400,000 Landsat 5,7 and 8 images for the 2010-2013 interval . The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.</p>

          <p>Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage.  Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.</p>
          <p><strong>2015 Update (Version 1.1)</strong></p>
          <p>This data set was recently updated and now includes a 2013 loss layer and revised layers for 2011 and 2012. The analysis method has been modified in numerous ways, and the update should be seen as part of a transition to a future “version 2.0” of this data set that is more consistent over the entire 2001 and onward period. Key changes include:</p>
          <ul class="bullets">
            <li>The use of Landsat 8 data for 2013 and Landsat 5 data for 2010-2011</li>
            <li>The reprocessing of data from 2011 to 2012 in measuring loss</li>
            <li>Improved training data for calibrating the loss model</li>
            <li>Improved per sensor quality assessment models to filter input data</li>
            <li>Improved input spectral features for building and applying the loss model</li>
          </ul>
          <p>These changes lead to a different and improved detection of global tree cover loss. However, the years preceding 2011 have not yet been reprocessed with the revised analysis methods, and users will notice inconsistencies between versions 1.0 (2001-2012) and 1.1 (2001-2013) as a result. It must also be noted that a full validation of the results incorporating Landsat 8 has not been undertaken. Such an analysis may reveal a more sensitive ability to detect and map forest disturbance using Landsat 8 data. If this is the case then there will be a more fundamental limitation to the consistency of this data set before and after the inclusion of Landsat 8 data. Validation of Landsat 8-incorporated loss detection is planned.</p>
          <p>Some examples of improved change detection in the 2011–2013 update include the following:</p>
          <ul class="bullets">
            <li>Improved detection of boreal forest loss due to fire</li>
            <li>Improved detection of smallholder rotation agricultural clearing in dry and humid tropical forests</li>
            <li>Improved detection of selective logging</li>
          </ul>
          <p>These are examples of dynamics that may be differentially mapped over the 2001-2013 period in Version 1.1. A version 2.0 reprocessing of the 2001 and onward record is planned, but no delivery date is yet confirmed.</p>
          <p>The original version 1.0 data remains available for download <a href='http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.0.html' target='_blank'>here</a>.</p>
          <p class='credits'><strong>Citation:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from:<a href='http://earthenginepartners.appspot.com/science-2013-global-forest' target='_blank'>http://earthenginepartners.appspot.com/science-2013-global-forest</a>.</p>

          <p class='credits'><strong>Suggested citations for data as displayed on GFW:</strong>Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Hansen/UMD/Google/USGS/NASA Tree Cover Loss and Gain Area.” University of Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html" %></em></p>
        </div>
      </div>
    </li>


    <li id='umd_tree_loss_gain_infographics' class='source-item first hide'>
      <div class='source_header'>
        <strong class='source_title'>Tree cover change statistics</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'><!--
          <a href='http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
         --></div>

        <div class='source_table'></div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>The infographics that appear in the country profile header were created using tree cover and tree cover loss and gain data from Hansen/UMD/Google/USGS/NASA. For more information on these data, please visit the <a href="http://data.globalforestwatch.org/" target="_blank">Open Data Portal</a>.</p>

          <p>The infographics can be viewed by country or by subnational boundary. Click "Select Jurisdiction" to view data for a different region.</p>

          <p>Due to variation in research methodology and/or date of content, tree cover and tree cover loss and gain statistics cannot be compared against each other. Accordingly, “net” loss cannot be calculated by subtracting tree cover gain from tree cover loss, and current (or post-2000) tree cover cannot be determined by subtracting annual tree cover loss from year 2000 tree cover.</p>

          <p>Please also be aware that “tree cover” does not equate to “forest cover.” “Tree cover” refers to the biophysical presence of trees, which may be a part of natural forests or tree plantations. Thus, loss of tree cover may occur for many reasons, including deforestation, fire, and logging within the course of sustainable forestry operations. Similarly, tree cover gain may indicate the growth of tree canopy within natural or managed forests.</p>

          <p>Finally, note that "Select Jurisdiction" and "Tree Cover Canopy Settings" only affect infographics within the top portion of the Country Profiles and do not apply to FORMA, Forest Type, People & Economy or to other parts of this page.</p>

          <p class='credits'><strong>Citation:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available online from: <a href='http://earthenginepartners.appspot.com/science-2013-global-forest' target='_blank'>http://earthenginepartners.appspot.com/science-2013-global-forest</a>.</p>

          <p class='credits'><strong>Suggested citations for data as displayed on GFW:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Hansen/UMD/Google/USGS/NASA Tree Cover and Tree cover Loss and Gain, Country Profiles.” University of Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org/' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p><em><a href='http://data.globalforestwatch.org/' target='_blank'>Learn more or download data</a>.</p></em>
        </div>
      </div>
    </li>




     <li id='forestgain' class='source-item '>
      <div class='source_header'>
        <strong class='source_title'>Tree cover gain</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(12 years, 30m, global, Hansen/UMD/Google/USGS/NASA)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies areas of tree cover gain</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>30 × 30 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global land area (excluding Antarctica and other Arctic islands)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://landsat.usgs.gov/' target='_blank'>Landsat 7 ETM+</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>12-year cumulative, updated annually</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2001–2012</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Tree cover canopy density</dt>
            <dd>>50%</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.</p>

              <p>When zoomed out (< zoom level 13), pixels of gain are shaded according to the density of gain at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).</p>
              <p>Read a summary of the accuracy of this data <a href='http://blog.globalforestwatch.org/2015/12/how-accurate-is-accurate-enough-examining-the-glad-global-tree-cover-change-data-part-1/'>here</a>.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set measures areas of tree cover gain across all global land (except Antarctica and other Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the <a href='http://landsat.usgs.gov/science_L7_cpf.php' target='_blank'>Landsat 7 thematic mapper plus (ETM+)</a> sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain.</p>



          <p>Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations. </p>

          <p class='credits'><strong>Citation:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.</p>

          <p class='credits'><strong>Suggested citations for data as displayed on GFW:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Hansen/UMD/Google/USGS/NASA Tree Cover Loss and Gain Area.” University of Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch. <a href='http://earthenginepartners.appspot.com/science-2013-global-forest' target='_blank'>http://earthenginepartners.appspot.com/science-2013-global-forest</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', sources_path %></em></p>
        </div>
      </div>
    </li>


    <li id='forma' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Forma alerts</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(monthly, 500m, humid tropics, WRI/CGD)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://forma.s3.amazonaws.com/1.0/forma_all.zip' target='_blank' title='Download'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Detects areas where tree cover loss is likely to have recently occurred</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>500 × 500 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Humid tropical forest biome</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Displayed on the GFW site as monthly alerts, but available for download in 16-day increments</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>January 2006-present</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Viewing data for the first period available via the time slider may include alerts that reflect detections from the February 2000 to December 2005 training period as the algorithm "catches up" post-training.</p>

              <p>The GFW team has clipped out some data in regions where data quality is suspected to be low because of persistent cloud cover over identified ecoregions. This covers parts of Liberia, Venezuela, Guyana, Vietnam, Laos, and Burma/Myanmar. The current extent of the FORMA alerts is available for viewing here—<a href='http://wri-01.cartodb.com/viz/3cc0f4c2-56d3-11e3-80bb-c8600054c0d1/embed_map?title=true&description=true&search=false&shareable=false&cartodb_logo=false&layer_selector=false&legends=false&scrollwheel=true&sublayer_options=1&sql=&zoom=3&center_lat=-1.845383988573187&center_lon=16.34765625' target='_blank'>FORMA geographic extent</a>.</p>

              <p>The <a href='https://github.com/wri/forma-clj' target='_blank'>algorithm</a> behind the FORMA alerts is constantly evolving to fix bugs and improve accuracy. As a result, what appears on the site and the results of analyses conducted on the site may change over time. It is important to always include the access date when citing FORMA. Analysis of FORMA alerts on the map should be considered definitive, as analysis is based on raw FORMA data, whereas the data on the map are optimized for visual display. For questions, please join the <a href='https://groups.google.com/forum/#!forum/globalforestwatch' target='_blank'>GFW discussion forum</a> or <a href='mailto:gfw@wri.org'>email us</a>.</p>

              <p>For the purpose of this study, “tree cover” was defined as areas with greater than 25% canopy cover (as determined by the <a href='http://journals.ametsoc.org/doi/abs/10.1175/1087-3562(2003)007%3C0001%3AGPTCAA%3E2.0.CO%3B2' target='_blank'>Vegetation Continuous Fields data set</a>), and change was measured without regard to forest land use. Tree cover assemblages that meet the 25% threshold include intact forests, plantations, and forest regrowth.</p>

              <p>When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>FORMA is a near real-time tree cover loss alert system. It uses a cloud computing <a href="https://github.com/reddmetrics/forma-clj" target="_blank">algorithm</a> to analyze frequently updated satellite imagery along with complementary information on factors that affect tree cover loss, such as fires and precipitation. The system generates twice-monthly “alerts” for the world’s <a href="http://wolfweb.unr.edu/~ldyer/classes/396/olsonetal.pdf" target="_blank">humid tropical forests</a> that identify 500 × 500 meter areas where new, large-scale loss is likely to have occurred.</p>

          <p>FORMA is designed for quick identification of new areas of tree cover loss. The system analyzes data gathered daily by the <a href="http://modis.gsfc.nasa.gov/about/" target="_blank">MODIS</a> sensor, which operates on NASA’s Terra and Aqua satellites. The FORMA alerts system then detects pronounced changes in vegetation cover over time, as measured by the <a href="http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php" target="_blank">Normalized Difference Vegetation Index (NDVI)</a>, a measure of vegetation greenness. These pronounced changes in vegetation cover are likely to indicate forest being cleared, burned, or defoliated.</p>

          <p>FORMA alerts only appear in areas where the probability of tree cover loss is greater than or equal to 50%.</p>

          <p>Upcoming upgrades to FORMA include improving the resolution to 250 × 250 meters, and expanding coverage to tropical dry forest and eventually to other biomes across the global scale.</p>

          <p class='credits'><strong>Citation:</strong> Hammer, Dan, Robin Kraft, and David Wheeler. 2013. “FORMA Alerts.” World Resources Institute and Center for Global Development. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', sources_path %></em></p>
        </div>

        <div class='source_extended'>
          <h3 class='overview_title'>Methodology</h3>
          <p>This data set uses freely available satellite imagery, collected by the <a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>Moderate Resolution Imaging Spectroradiometer (MODIS)</a>, which operates on NASA's Terra and Aqua (EOS PM) satellite platforms and views the entire Earth’s surface every 1 to 2 days. The images help to reveal notable changes in vegetation cover over time using an indicator that measures vegetation intensity called the <a href='http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php' target='_blank'>Normalized Difference Vegetation Index (NDVI)</a>. By applying an automated algorithm to this input and in combination with complementary data inputs for fire hotspots and global precipitation averages, areas where tree cover loss is likely to have occurred are therefore identified. The algorithm also employs parallel processing in a remote server system the cloud that enables the rapid analysis of these very large data sets.</p>

          <h3 class='overview_title'>Explanation</h3>
          <p>Tree cover loss alerts appear when and where new, large-scale loss is likely to have occurred after 2005. Thus the alerts should not be interpreted as an analysis of total tree cover loss area but rather as an indication of an area that has a high probability of having experienced tree cover loss or disturbance over time. The system employs advanced statistical techniques to achieve the best fit to scientifically validate information on loss, measured as a probability. On the GFW website, alerts appear only for areas where there is a 50% or higher probability of tree cover loss. That is, alerts appear for a particular period when there has been significant loss in the area during or before that period.</p>

          <h3 class='overview_title'>Temporal and spatial resolution</h3>
          <p>FORMA alerts are displayed on the GFW site as monthly data, and the site is updated each month. Users who download data for analysis will find that the underlying data set is actually available at 16-day intervals, intervals that do not line up perfectly with calendar months. Data is displayed at a monthly resolution on GFW due to the monthly availability of precipitation data input. Users can manipulate the GFW time slider to view trends in loss alerts from December 2005 to present.</p>

          <p>The alert system currently identifies 500 × 500 meter areas where loss is statistically likely to have occurred. Alerts for a 250 meter resolution version are currently being developed on Google’s Earth Engine platform and will be available in 2014.</p>

          <h3 class='overview_title'>Geographic extent</h3>
          <p>FORMA alerts are currently available only for humid tropical forests (as defined by <a href='http://www.pnas.org/content/105/27/9439.full#ref-1' target='_blank'>Hansen et al. (2008)</a>, based on <a href='http://wolfweb.unr.edu/~ldyer/classes/396/olsonetal.pdf' target='_blank'>WWF’s terrestrial ecoregions</a>) spanning portions of 89 countries. The development team is working to incorporate additional data to extend the geographic coverage beyond the current extent. To visualize the geographic extent of the alerts on the map, switch on the “Humid Tropical Forest Biome” layer.</p>

          <h3 class='overview_title'>Data applications</h3>
          <p>The alerts have been designed for quick identification of tree cover loss as it happens. This allows for rapid response and prioritization of scarce financial and human resources dedicated to forest conservation or sustainable forest management. Armed with this information, stakeholders can use preemptive methods such as on-the-ground visits or aerial inspection with high-resolution satellite imagery (less than 5-meter pixel resolution) to investigate suspected tree cover loss areas.</p>

          <p>In addition, the alerts may be of value to a variety of researchers who study both temporal and spatial patterns related to tree cover loss areas.</p>

          <p>Using the GFW platform, the alerts can be compared against other relevant data layers, such as protected areas and concessions boundaries, to evaluate the effectiveness of forest management practices across time and spatial extent.</p>

          <h3 class='overview_title'>Accuracy and validation</h3>
          <p>Inaccuracies are an inherent part of remote sensing analysis. FORMA alerts appear in areas with a greater than 50% probability of tree cover loss, based on the algorithm described under Methodology. However, persistent cloud cover is a continuous issue in the tropics, and extreme flooding can also produce unreliable remotely sensed data that will result in tree cover loss “false positives” (alerts where no actual tree cover loss has occurred). Furthermore, the alerting system cannot detect all forest cover loss, whether due to the small size of the loss area, persistent cloud cover, or other explanations still being identified through GFW validation efforts.</p>

          <p>The major instances of false positives may occur as the following:</p>

          <ol class='bullets'>
            <li><strong>A random, "speckled" distribution of alerts across an ecoregion, or complete filling of a small ecoregion.</strong> Caused by limited or sparse training data, particularly in small ecoregions, which makes it difficult to tune the model there. As a result, alerts cannot be reliably detected. In a normal ecoregion, alerts are usually clustered.</li>

            <li><strong>A rapid explosion of alerts over 1-3 months covering a relatively large area.</strong> Caused by a significant, persistent drop in detected vegetation levels due to persistent cloud cover along coastlines, in mountains, or elsewhere.</li>

            <li><strong>Alerts in water.</strong> Caused by shifting water bodies. These alerts should be considered not necessarily as false positives but rather as ambiguous alerts requiring additional data for corroboration.</li>
          </ol>

          <p>The GFW team is working aggressively to address potential inaccuracies in the data through rigorous validation methods. Specifically, the GFW team is comparing the growing data set of historical alerts to other validated data sets, which are being used for similar applications.</p>

          <p><a href='http://www.wri.org/publication/satellite-based-forest-clearing-detection-brazilian-amazon' target='_blank'>This issue brief</a> demonstrates the spatial correlation of the alerts with the PRODES and DETER data sets, produced by the Brazilian Space Agency for the Amazon. The conclusions from the working paper help to illustrate the potential pitfalls of the algorithm, along with its strengths. Through future refinement and proposed crowdsourcing efforts, the GFW team expects the data quality of the FORMA Alerts will continue to improve.</p>

          <h3 class='overview_title'>Additional resources on FORMA</h3>
          <p><strong>Working Papers:</strong></p>

          <ul class='bullets'>
            <li>FORMA: Forest Monitoring for Action – Rapid Identification of Pan-tropical Deforestation Using Moderate-Resolution Remotely Sensed Data (CGD Working Paper 192, November 2009) [<a href='http://www.cgdev.org/sites/default/files/1423248_file_Hammer_Kraft_Wheeler_FORMA_FINAL.pdf' target='_blank'>pdf</a>]</li>
            <li>From REDD to Green: A Global Incentive System to Stop Tropical Forest Clearing (CGD Working Paper 282, December 2011) [<a href='http://www.cgdev.org/sites/default/files/1425830_file_Wheeler_et_al_REDD_to_Green.pdf' target='_blank'>pdf</a>]</li>
            <li>Forest Clearing in the Pantropics: December 2005-2011 (CGD Working Paper 283, December 2011) [<a href='http://www.cgdev.org/publication/forest-clearing-pantropics-december-2005%E2%80%93august-2011-working-paper-283' target='_blank'>pdf</a>] Data Set for Working Paper 283</li>
            <li>FORMA and fCPR: Accelerating a Performance-Based Payment System for REDD+ (CGD Policy Paper 006, June 2012) [<a href='http://www.cgdev.org/publication/forma-and-fcpr-accelerating-performance-based-payment-system-redd' target='_blank'>pdf</a>]</li>
            <li><a href='http://www.wri.org/publication/satellite-based-forest-clearing-detection-brazilian-amazon' target='_blank'>Satellite-Based Forest Clearing Detection in the Brazilian Amazon: FORMA, DETER, and PRODES</a> (WRI Working Paper, February 2014)</li>
          </ul>

          <p><strong>Publications:</strong></p>
          <ul class="bullets">
            <li>Alerts of forest disturbance from MODIS imagery (International Journal of Applied Earth Observation and Geoinformation 33 (2014) 1–9) [<a href='https://s3.amazonaws.com/gfw-files/Hammer+et+al+2014.pdf' target='_blank'>pdf</a>]</li>
          </ul>

          <p><strong>GitHub Repositories:</strong></p>
          <ul class='bullets'>
            <li><a href='https://github.com/reddmetrics/forma-clj' target='_blank'>REDD Metrics FORMA page</a></li>
          </ul>
        </div>
      </div>

      <div class='source_coverage_header'>
        <div class='source_coverage_download'>
          <h3 class='source_coverage_title'>Geographic coverage of FORMA alerts</h3> <a href='https://wri-01.cartodb.com/tables/forma_extent' target='_blank' title='Download on CartoDB' class='source_download'>Download<i class='arrow_down'></i></a>
        </div>
      </div>

      <div class='source_coverage'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays the geographic coverage of FORMA alerts</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Regional</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>This data layer shows the geographic coverage of FORMA alerts, which largely corresponds to the extent of the humid tropical forest biome, as defined by <a href='http://www.pnas.org/content/105/27/9439.full#ref-1' target='_blank'>Hansen et al. (2008)</a>, and based on <a href='http://wolfweb.unr.edu/~ldyer/classes/396/olsonetal.pdf' target='_blank'>WWF’s terrestrial ecoregions</a>. The biome illustrated by this layer includes a number of smaller forest ecoregions, which span portions of 89 countries.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a>, validated with <a href='http://landsat.usgs.gov/' target='_blank'>Landsat</a> and <a href='http://www.cbers.inpe.br/ingles/' target='_blank'>CBERS</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2001</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <p class='credits'><strong>Citation:</strong> Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W.W. Wettengel, P. Hedao, and K. R. Kassem. 2001. “Terrestrial Ecoregions of the World: A New Map of Life on Earth.” <em>BioScience</em> 51, no. 11 (November): 933–38.</p>

          <p class='read_more hidden'><em>Download from <a href='https://wri-01.cartodb.com/tables/wwf_terr_ecoregions/public' target='_blank'>CartoDB</a></em></p>
        </div>
      </div>
    </li>

    <li id='imazon' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SAD alerts</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(monthly, 250m, Brazilian Amazon, Imazon)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.imazongeo.org.br/downloads/' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Deforestation alert system that monitors forest cover loss and forest degradation</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>250 × 250 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Brazilian Amazon</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a>, validated with <a href='http://landsat.usgs.gov/' target='_blank'>Landsat</a> and <a href='http://www.cbers.inpe.br/ingles/' target='_blank'>CBERS</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Monthly</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>January 2007–present</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>The Deforestation Alert System (Sistema de Alerta de Desmatamento—SAD) is a monthly alert that monitors forest cover loss and forest degradation in the Brazilian Amazon. The system generates information that is published monthly by <a href='http://www.imazon.org.br/' target='_blank'>Imazon</a>, a Brazilian NGO, through its <em>Forest Transparency Bulletin</em>. The monthly alerts are derived from a temporal mosaic of <a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a> daily images that are scaled down from 500 × 500 meter to 250 × 250 meter resolution. The monthly results are then validated using medium resolution images from the China-Brazil Earth Resources Satellite (<a href='http://www.cbers.inpe.br/ingles/' target='_blank'>CBERS</a>) and NASA <a href='http://landsat.usgs.gov/' target='_blank'>Landsat</a> data in order to “ground-truth” the results being reported.</p>

          <p class='credits'><strong>Citation:</strong> Souza, C. M., S. Hayashi, and A. Veríssimo. 2009. “Near Real-Time Deforestation Detection for Enforcement of Forest Reserves in Mato Grosso.” <em>FIG—Land Governance in Support of the MDGS: Responding to New Challenges.</em> <a href='http://www.fig.net/pub/fig_wb_2009/papers/trn/trn_2_souza.pdf' target='_blank'>www.fig.net/pub/fig_wb_2009/papers/trn/trn_2_souza.pdf</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “SAD Alerts.” Imazon. Accessed through Global Forest Watch on [date].<a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', sources_path %></em></p>
        </div>
      </div>

      <div class='source_coverage_header'>
        <div class='source_coverage_download'>
          <h3 class='source_coverage_title'>Geographic coverage of Imazon SAD alerts</h3> <a href='http://wri-01.cartodb.com/tables/imazon_sad_geografic_extent/public' target='_blank' title='Download on CartoDB' class='source_download'>Download<i class='arrow_down'></i></a>
        </div>
      </div>

      <div class='source_coverage'>
        <p>The geographic coverage of Imazon SAD alerts is the Legal Amazon (political) excluding the Brazilian state of Maranhão.</p>

        <p class='read_more hidden'><em>Download data from <a href='http://wri-01.cartodb.com/tables/imazon_sad_geografic_extent/public' target='_blank'>CartoDB</a></em></p>
      </div>
    </li>

    <li id='modis' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>QUICC alerts</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(quarterly, 5km, &lt;37 degrees north, NASA)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://wri-01.cartodb.com/api/v2/sql?q=SELECT%20*%20FROM%20quicc_alerts%20&format=shp' target='_blank' title='Download on CartoDB'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies areas of land that have lost at least 40% of their green vegetation cover from the previous quarterly product</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>5 × 5 kilometers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global, except for areas >37 degrees north</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Quarterly (April, July, October, January)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>October 2011–present</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>The data represents an indicator of vegetation cover change, not necessarily tree or forest cover loss.</p>
              <p>When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>

          <p>The <a href='http://geo.arc.nasa.gov/sge/casa/latest.html' target='_blank'>Quarterly Indicator of Cover Change (QUICC)</a> product was developed at the NASA Ames Research Center by the <a href='http://geo.arc.nasa.gov/sge/casa/' target='_blank'>CASA</a> ecosystem modeling team. QUICC compares the MODIS global vegetation index (VI) images at the exact same time period each year on a quarterly basis (end of March, June, September, and December) and identifies land areas that have lost at least 40% of their green vegetation from the previous product and over the past year. This level of green vegetation loss is commonly associated with major forest or tree cover loss.</p>

          <p>The CASA team updates the global QUICC products as soon as the newest quarterly MODIS worldwide VI image is produced. After September (Q3) 2012, the data were adjusted to exclude forest areas located >37 degrees north as a result of confusion caused by year-to-year variations in seasonal snow cover.</p>

          <p class='credits'><strong>Citation:</strong> NASA-CASA Project. “CASA ‘Quarterly Indicator of Cover Change’ (QUICC).” Accessed on [date].<a href='http://geo.arc.nasa.gov/sge/casa/latest.html' target='_blank'>geo.arc.nasa.gov/sge/casa/latest.html</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “QUICC Alerts.” NASA Ames Research Center and California State University. Accessed through Global Forest Watch on [date].<a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', sources_path %></em></p>
        </div>
      </div>

      <div class='source_coverage_header'>
        <h3 class='source_coverage_title'>Geographic coverage of QUICC alerts</h3>
      </div>

      <div class='source_coverage'>
        <p>The geographic coverage of QUICC alerts is global, except for areas >37 degrees north.</p>
      </div>
    </li>

    <li id='terrailoss' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Terra-i Alerts</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(monthly, 250m, Latin America, CIAT)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.terra-i.org/terra-i/data.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Detects areas in Latin American where tree cover loss is likely to have recently occurred</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Resolution/scale</dt>
            <dd>250 × 250 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Latin America</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and water body’ presence data; Tropical Rainfall Measuring Mission (TRMM) precipitation data</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Monthly</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd> 2004 – present</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Given the lack of ground-based data, the methodology was validated using data from other forest monitoring systems such as PRODES (<a href="http://www.obt.inpe.br/prodes/index.php" target="_blank">http://www.obt.inpe.br/prodes/index.php</a>) which have been validated separately.</p>
              <p>All clouds, water, and mist were masked based on MODIS Quality Assessment and MOD35 products and their values changed to “No Data”.</p>
              <p>The Terra-i algorithm for change detection does not automatically identify events that occurred because of wild fires or within secondary forests or oil palm plantations. Furthermore, the moderate resolution of the MODIS sensor does not detect small scale events (<5ha). Terra-i is intended to be used to quickly identify deforestation hotspots which should then be more thoroughly investigated with higher resolution imagery or field validation.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>

          <p>Terra-i is a near real-time monitoring system that detects land cover changes in Latin America. It uses satellite data from MODIS vegetation indices (MOD13Q1 and NDVI) and products related to presence of water bodies (MOD35) as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data to detect anthropogenic changes in vegetation cover every 16 days. Terra-i is a collaboration between the International Center for Tropical Agriculture (CIAT - DAPA), CGIAR’s Research Program on Forestry, Trees and Agroforestry (FTA), The Nature Conservancy (TNC), the University of Applied Sciences Western Switzerland (HEIG-VD), and King’s College London (KCL).</p>

          <p>The system, which uses a computational algorithm similar to FORMA (<a href="http://www.globalforestwatch.org/sources/forest_change#forma">http://www.globalforestwatch.org/sources/forest_change#forma</a>), is based on the premise that natural vegetation follows a predictable pattern of change in greenness from one date to the next, brought about by site-specific land and climatic conditions over the same period. The model is trained to understand the normal pattern of changes in vegetation greenness in relation to terrain and rainfall for a site, which allows for prediction of what the next vegetation response should be based on the historical data. If the prediction is significantly different from the historical responses in relation to pattern of rainfall and lasts for two 16-day periods in a row, the pixel is marked as potentially having changed by anthropogenic means.</p>


          <p class="credits"><strong>Citation:</strong>Reymondin, Louis, Andrew Jarvis, Andres Perez-Uribe, Jerry Touval, Karolina Argote, Julien Rebetez, Edward Guevara, and Mark Mulligan. 2012. “Terra-i: A methodology for near real-time monitoring of habitat change at continental scales using MODIS-NDVI and TRMM.” CIAT-Terra-i. <a href='http://terra-i.org/dms/docs/reports/Terra-i-Method/Terra-i%20Method.pdf' target='_blank'>http://terra-i.org/dms/docs/reports/Terra-i-Method/Terra-i%20Method.pdf</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> Reymondin, Louis, Andrew Jarvis, Andres Perez-Uribe, Jerry Touval, Karolina Argote, Julien Rebetez, Edward Guevara, and Mark Mulligan. 2012. “Terra-i alerts." CIAT-Terra-i. Accessed through Global Forest Watch on [date].<a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id='forest_cover' class='forest_cover source-article'>
  <h2 class='source_category_title'>Forest cover</h2>
  <p class='source_category_description'>Forest cover data measure the extent of forests, forest ecosystems, and related features such as carbon stocks.</p>

  <ul class='sources'>
    <li id='forest2000' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Tree cover</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies areas of tree cover</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>30 × 30 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global land (excluding Antarctica and Arctic islands)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://landsat.usgs.gov/' target='_blank'>Landsat 7 ETM+</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Update coming in 2015</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Tree cover canopy density</dt>
            <dd>Varies according to selection (click the gear icon on the map to change the minimum tree cover canopy density threshold)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).</p>
          <p>Data in this layer were generated using multispectral satellite imagery from the <a href="http://landsat.usgs.gov/" target="_blank">Landsat 7 thematic mapper plus (ETM+)</a> sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.</p>

          <p class='credits'><strong>Citation:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” <em>Science</em> 342 (15 November): 850–53.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Tree Cover.” University of Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_cover" %></em></p>
        </div>
      </div>
    </li>
    <li id='pantropical' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Tropical forest carbon stocks</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Shows total carbon stock values in live biomass across the tropics</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1 × 1 kilometer (100 ha)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Tropics</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://icesat.gsfc.nasa.gov/icesat/glas.php' target='_blank'>ICESAT GLAS Lidar</a> and other radar satellite imagery</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Early 2000s</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>The forest above and below ground biomass were produced at 1 × 1 kilometer (100 ha) spatial resolution. The values for each pixel (1 × 1 km) refer to the biomass of forests remaining within the pixel and can be used directly to estimate total carbon stock in forests within one pixel. Along with the biomass values, there is an error map at the same spatial resolution providing the uncertainty in biomass estimation. It is recommended that both biomass and uncertainty values be used together for carbon assessments and verifications. The map will provide accurate estimates of total carbon stock and carbon density when aggregated to large areas (5,000-10,000 ha) for project and regional level assessments. The biomass value of a single pixel may have large uncertainty when compared with small plots for verification.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>The Tropical Forest Carbon Stocks data layer shows a “benchmark” map of biomass carbon stocks for more than 2.5 billion hectares of forests on three continents, encompassing all tropical forests, for the early 2000s. Researchers from NASA’s <a href='http://www.jpl.nasa.gov/' target='_blank'>Jet Propulsion Laboratory (JPL)</a> and <a href='http://www.winrock.org/' target='_blank'>Winrock International</a> developed the layer by collecting data from more than 4,000 in situ inventory plots, plus optical and microwave imagery (1 km resolution) to extrapolate the amount of calculated carbon stocks from forests distributed over the South American, Southeast Asia, and the sub-Saharan African regions. Further data on tree height and landscape characteristics across the tropics were derived using satellite light detection and ranging (Lidar) data from the <a href='http://icesat.gsfc.nasa.gov/icesat/glas.php' target='_blank'>ICESAT GLAS Lidar</a>. This data set also helps illustrate regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.</p>

          <p class='credits'><strong>Citation:</strong> Saatchi, S.S., N.L. Harris, S. Brown, M. Lefsky, E. Mitchard, W. Salas, B. Zutta, W. Buermann, S. Lewis, S. Hagen, S. Petrova, L. White, M. Silman, and A. Morel. 2011. “<a href='http://www.pnas.org/content/108/24/9899.full' target='_blank'>Benchmark map of forest carbon stocks in tropical regions across three continents</a>.” <em>Proceedings of the National Academy of Sciences</em> vol 108, no. 24, pp 9899-9904. DOI: 10.1073/pnas.1019576108.</p>

          <p class='read_more'><em>Learn more or download data at the <a href='http://www.pnas.org/content/suppl/2011/05/24/1019576108.DCSupplemental' target='_blank'>NASA Jet Propulsion Laboratory website</a>.</em></p>
        </div>
      </div>
    </li>
    <li id='ifl_2013_deg' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Intact Forest Landscapes 2000/2013</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://intactforests.org/data.ifl.html' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies the world’s last remaining undisturbed forest areas, large enough to retain all native biodiversity and showing no signs of human activity as of the year 2013 and their degradation from 2000-2013.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Approximately 1:100,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://landsat.usgs.gov/' target='_blank'>Landsat TM/ETM+</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>2014 update; 2006 original publication</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2013</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>The world IFL map was created through visual interpretation of Landsat images by experts and may contain inconsistencies and inaccuracies because of limitations in the spatial resolution of the imagery and lack of ancillary information about local land-use practices in some regions. In addition, the treatment of fire as a form of degradation in the IFL methodology may vary from the understanding of fire dynamics in different regions and ecozones.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable to allow for repeated assessments over time as well for verification. The criteria were separated into two groups to assess the spatial extent of developed areas and the degradation of these areas. These were then mapped and identified from Landsat satellite imagery for the year 2013. Degraded areas were identified as areas that had changed from 2000-2013 based on comparison with the 2000 IFL data – either through tree cover loss or fragmentation by roads or other infrastructure. The landscapes identified as unfragmented were at least 50,000 hectares in size with a minimum width of 10 kilometers. This data can be used to assess forest intactness, alteration, and degradation at global and regional scales. More information about the data set and methodology is available on <a href="http://www.intactforests.org">www.intactforests.org</a></p>
          <p class='credits'><strong>Citation:</strong> Potapov P., Yaroshenko A., Turubanova S., Dubinin M., Laestadius L., Thies C., Aksenov D., Egorov A., Yesipova Y., Glushkov I., Karpachevskiy M., Kostikova A., Manisha A., Tsybikova E., Zhuravleva I. 2008. Mapping the World's Intact Forest Landscapes by Remote Sensing. Ecology and Society, 13 (2) http://www.ecologyandsociety.org/vol13/iss2/art51/ </p>

          <p class='credits'><strong>Suggested citations for data as displayed on GFW:</strong> Greenpeace, University of Maryland, World Resources Institute and Transparent World. 2014. Intact Forest Landscapes: update and degradation from 2000-2013. Accessed through Global Forest Watch on [date]. www.globalforestwatch.org</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', sources_path %></em></p>
        </div>
      </div>
    </li>
    <li id='idn_primary' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Indonesia primary forest</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://glad.geog.umd.edu/indonesia/data2014/' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd> Identifies the location of intact and degraded primary forests across Indonesia as of the year 2000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>30 × 30 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://landsat.usgs.gov/' target='_blank'>Landsat</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer represents a draft visualization of the data. Artifacts such as white vertical lines, or visually degraded pixels may occur temporarily. Also note that definition of primary forests used to create this layer may not match other definitions of primary forests. See the overview section for details.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set indicates the location of intact and degraded primary forests across Indonesia as of the year 2000. Primary forest consist of mature natural forest cover that has not been completely cleared in recent history (30 years or more) and consisted of a contiguous block of 5 ha or more. Primary forest cover was mapped using Landsat composites and multi-temporal metrics as input data to a two-step supervised classification. The first step was a per-pixel classification of areas with tree canopy cover of 30% and above for the 2000 reference year.</p>
          <p>A second per-pixel classification procedure was performed to separate primary forest from other tree cover for 2000; contiguous areas of 5 ha and greater were retained as primary forest. A limited editing of this classification was performed to remove older plantations and adjust other forest formations that could not be identified using the per-pixel classifier, but could be identified in photo-interpretive contexts. Primary forests were subsequently characterized into primary intact and primary degraded subclasses using the GIS-based buffering approach of the Intact Forest Landscapes (IFL). To create the IFL layer, buffers of roads, settlements and other signs of human landscape alteration were used to identify degraded areas within zones of primary forest cover. IFL mapping employed cloud-free Landsat mosaics to quantify changes in primary intact forest extent. The map of primary intact and primary degraded forest cover types corresponds to the Indonesia Ministry of Forestry’s primary and secondary forest cover types.</p>

          <p class='credits'><strong>Citation:</strong> Margono, B. Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change,doi:10.1038/nclimate2277. Retrieved June 30, 2014, from <a href="http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate2277.html">Nature</a></p>

          <!--<p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Tree Cover.” University of Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>-->

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_cover" %></em></p>
        </div>
      </div>
    </li>
    <li id='mangrove' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Mangroves</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://data.unep-wcmc.org/datasets/4' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Shows the global distribution of mangrove forests, derived from earth observation satellite imagery</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Mapped from 30m Landsat imagery</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Landsat Global Land Survey Collection</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None planned</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1997-2000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>Results were validated using existing distribution data and published literature.</p><p>Note that small patches (< 900-2,700 m<sup>2</sup>) of mangrove forests cannot be identified using this approach. This methodological approach had a number of challenges, such as cloud cover and noise. There may also be areas where land cover was misclassified.</p><p>As the data set may still contain overlapping polygons, a dissolve operation (within a GIS) might be needed before surface area calculations are carried out.</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>To improve scientific understanding of the extent and distribution of mangrove forests of the world the status and distribution of global mangroves were mapped using recently available Global Land Survey (GLS) data and the Landsat archive.The project interpreted approximately 1,000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.</p>
          <p>The total area of mangroves in the year 2000 was 137,760 square kilometers in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.</p>
          <p>The remaining area of mangrove forest in the world is less than previously thought; the estimate provided in this study is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. This data set presents the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created</p>

          <p class='credits'><strong>Citation:</strong> Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography 20: 154-159.</p>
          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_cover" %></em></p>
        </div>
      </div>
    </li>
    <li id='tree-cover-density' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Tree cover density</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class="source_download">
          <a href="http://www.earthenginepartners.appspot.com/science-2013-global-forest/download.html" target="_blank" title="Download at project website">Download at project website<i class="arrow_down"></i></a>
        </div>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays the extent of tree cover as defined by percent tree cover canopy density</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>30 × 30 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global land (excluding Antarctica and Arctic islands)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href="http://landsat.usgs.gov/" target="_blank">Landsat 7 ETM+</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities.</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This data set displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).</p>
          <p>Data in this layer were generated using multispectral satellite imagery from the <a href="http://landsat.usgs.gov/" target="_blank">Landsat 7 thematic mapper plus (ETM+)</a> sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.</p>
          <p class="credits"><strong>Citation:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” <em>Science</em> 342 (15 November): 850–53.</p>
          <p class="credits"><strong>Suggested citation for data as displayed on GFW:</strong> Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Tree Cover.” University of Maryland and Google. Accessed through Global Forest Watch on [date]. <a href="http://www.globalforestwatch.org" target="_blank">www.globalforestwatch.org</a>.</p>
          <p class="read_more hidden"><em><a href="/sources#forest_cover">Learn more or download data</a></em></p>
        </div>
      </div>
    </li>
    <li id='peat-lands' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Peat Lands</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays peat land, classified by depth.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000 scale</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Wetlands International</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2002</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>Peat land data is geographically limited to Kalimantan.</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the peat land in Indonesia, classified by depth (1:250,000 scale). Depth categories (in cm): 0, &lt;50, 50-100; 100-200; 200-400; 400-800; 800-1200. Dataset was digitized by Sekala (a local non-profit organization), atlas of peat land distribution Kalimantan (Wetlands International, 2004). Maps are available digitally online at <a href="http://www.wetlands.or.id/publications_maps.php/" target="_blank">wetlands</a>. This dataset was prepared by the World Resources Institute for use in the Interactive Atlas of Indonesia's Forests (2009).</p>
          <p class="credits"><strong>Citation:</strong> Wetlands International (2004). Data prepared by the World Resources Institute and is available in Minnemeyer et al. (2009). Interactive Atlas of Indonesia's Forests CD-ROM. Washington, DC: World Resources Institute.</p>
        </div>
      </div>
    </li>
    <li id='land-cover-south-east-asia' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>LAND COVER SOUTH EAST ASIA</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Indicates strata of22 land cover types over three temporal periods.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>300m</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Landsat 4, 5, and 7</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2002</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Using Landsat images, land use change was visually interpreted using 22 different land cover types. The analysis spans the time periods of 1990 to 2000, 2001 to 2005, and 2006 to 2009/2010, focused on large-scale palm oil plantations. Land was classified using criteria from the Ministry of Forestry and the ministry of Agriculture in Indonesia.</p>
          <p>For more information, see <a href="http://www.rspo.org/file/GHGWG2/4_oil_palm_and_land_use_change_Gunarso_et_al.pdf" target="_blank">Gunarso et al. 2013</a></p>
          <p class="credits"><strong>Citation:</strong> Gunarso, Petrus, Manjela Eko Hartoyo, Fahmuddin Agus, and Timothy J. Kileen. 2013. “Oil Palm and Land use Change in Indonesia, Malaysia, and Papua New Guinea.” Roundtable on Sustainable Palm Oil. Available online at: <a href="http://www.rspo.org/file/GHGWG2/4_oil_palm_and_land_use_change_Gunarso_et_al.pdf" target="_blank">Gunarso et al. 2013</a></p>
        </div>
      </div>
    </li>

    <li id='land-cover-global' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>LAND COVER GLOBAL</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays land cover classified by type.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>300m</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>GlobCover Land Cover v2 2008</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2008</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>Unmasked clouds may remain in the imagery. Additionally some pixels surrounding permanent snow areas may appear as snow even during no-snow periods. Some water masking occurs in which land is clipped in inland water areas, and the process for removing haze leaves some areas with patchy step changes. Last, some bright surface areas that appear as strong reflectors or deserts may be omitted as clouds.</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview (from GlobCover):</h3>
          <p>At 300 m resolution, GlobCover Land Cover v2 provides high resolution imagery of global land cover. The data contain 22 classes of land cover, drawing on the UN Land Cover Classification System. Satellite imagery comes from the ENVISAT satellite mission’s MERIS sensor, covering the period from December 2004 to June 2006.</p>
          <p class="credits"><strong>Citation:</strong> Bontemps, Sophie, Pierre Defourney, Eric Van Bogaert, Olivier Arion, Vasileios Kalogirou, and Jose Ramos Perez. 2009. “GLOBCOVER 2009: Product Description and Validation Report.” Available online at: <a href="http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf" target="_blank">http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf</a></p>
        </div>
      </div>
    </li>
    <li id='land-cover-indonesia' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>LAND COVER INDONESIA</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Indicates land cover, classified by type for the area of Indonesia.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Ministry of Forestry Indonesia and Landsat 2005 and 2006</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2006</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>The original land cover categories from the Ministry of Forestry were reclassified by the World Resources Institute for use in the Suitability Mapper.</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview:</h3>
          <p>This layer shows 2006 land cover, classified by type. The data is sourced from 2009 Ministry of Forestry data (1:250,000 scale). The World Resources Institute reclassified the original land cover categories from the Ministry of Forestry dataset for use in the Suitability Mapper (2012), into the following categories:</p>
          <ul class="bullets">
            <li>Primary dry land forest, primary mangrove forest, primary swamp forest --&gt; "primary forest"</li><li>Secondary dry land forest, secondary mangrove forest, secondary swamp forest --&gt; "secondary forest"</li>
            <li>Swamp, swamp scrubland --&gt; "wetland"</li>
            <li>Bare land, savannah, scrubland --&gt; "grassland/shrub"</li>
            <li>HTI, plantation --&gt; "plantations"</li>
            <li>Dry rice land, dry rice land mixed with scrub, rice land, fish pond --&gt; "agriculture"</li>
            <li>Mining, airport/harbor, settlement, transmigration area --&gt;"settlements/other land use"</li>
            <li>Bodies of water, cloud --&gt; excluded</li>
          </ul>
          <p>Exact definitions and descriptions of the methodologies used to produce this data are not available.</p><p>Original data available at http://appgis.dephut.go.id/appgis/kml.aspx under “Penutupan Lahan 2009."</p>
          <p>Exact definitions and descriptions of the methodologies used to produce this data are not available. Original data available at http://appgis.dephut.go.id/appgis/kml.aspx under “Penutupan Lahan 2009."</p>
          <p class="credits"><strong>Citation:</strong> Bontemps, Sophie, Pierre Defourney, Eric Van Bogaert, Olivier Arion, Vasileios Kalogirou, and Jose Ramos Perez. 2009. “GLOBCOVER 2009: Product Description and Validation Report.” Available online at: <a href="http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf" target="_blank">http://due.esrin.esa.int/globcover/LandCover2009/GLOBCOVER2009_Validation_Report_2.2.pdf</a></p>

        </div>
      </div>
    </li>

    <li id='legal-classifications' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>LEGAL CLASSIFICATIONS</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Indicates the legal classification of land in Indonesia.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Indonesia Ministry of Forestry</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Downloaded March 2010</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p></p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview:</h3>
          <p>Data from the Indonesia Ministry of Forestry shows the land classification for land in Indonesia. Land is classified as convertible production forest, limited production forest, non forest land, production forest, protection forest, and conservation forest.</p>
          <p class="credits"><strong>Citation:</strong> Downloaded from <a href="http://appgis.dephut.go.id/appgis/kml.aspx" target="_blank">http://appgis.dephut.go.id/appgis/kml.aspx</a>. Processed and provided by Greenpeace. Prepared by the World Resources Institute (2012)</p>
        </div>
      </div>
    </li>


  </ul>
</article>

<article id='forest_use' class='forest_use source-article'>
  <h2 class='source_category_title'>Forest use</h2>
  <p class='source_category_description'>Forest use data measure human use of forests, especially the boundaries of areas allocated for a certain land use through concessions, licenses, permits, titles, management units, etc.</p>

  <ul class='sources'>
    <li id='logging' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Logging</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='https://gfw2_download.s3.amazonaws.com/logging.zip' target='_blank' title='Download'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by governments to companies for logging</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Cameroon, Canada, Central African Republic, Democratic Republic of the Congo (DRC), Equatorial Guinea, Gabon, Republic of the Congo, Liberia, and Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of government documents, satellite imagery, and GPS data.<span class='hidden'> For information on country-specific concessions data please refer to the <%= link_to 'Data page', "#{sources_path}#forest_use" %>.</span></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not be comprehensive of all existing concessions in a country, and the location of certain concessions can be inaccurate.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Logging concession” refers to an area allocated by a government for logging in a public forest. Logging concessions are distinct from wood fiber concessions, where plantation forests are established for the production of pulp and paper products. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber from public forests; terminology varies at the national level, however, and includes "forest permits," "tenures," "licenses," and other terms.</p>

          <p>The logging concession data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>

          <p>Logging concession data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations.<span class='hidden'> See the <%= link_to 'Data page', "#{sources_path}#forest_use" %> for details on specific data sets.</span></p>

          <p>If you are aware of concession data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Logging.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org/' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently includes logging concession data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_logging">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_logging'>
              <ul>
                <li><a href='#' data-slug="cameroon_forest_management_units">Cameroon forest management units</a></li>
                <li><a href='#' data-slug="canada_forest_tenures">Canada forest tenures</a></li>
                <li><a href='#' data-slug="central_african_republic_logging_permits">Central African republic logging permits</a></li>
                <li><a href='#' data-slug="democratic_republic_of_the_congo_forest_titles">Democratic Republic of the Congo forest titles</a></li>
                <li><a href='#' data-slug="equatorial_guinea_logging_concessions">Equatorial Guinea logging concessions</a></li>
                <li><a href='#' data-slug="gabon_forest_licenses">Gabon forest licenses</a></li>
                <li><a href='#' data-slug="indonesia_logging_concessions">Indonesia logging concessions</a></li>
                <li><a href='#' data-slug="liberia_logging_concessions">Liberia logging concessions</a></li>
                <li><a href='#' data-slug="republic_of_the_congo_logging_concessions">Republic of the Congo logging concessions</a></li>
              </ul>
            </div>

            <div id="cameroon_forest_management_units" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Cameroon/GIS_Data/cmr_ufa_2013.zip'>Download</a></p>

              <p>Logging in Cameroon’s forests is permitted within Forest Management Units (FMUs). This data set displays the FMUs within Cameroon’s permanent forest estate. Selective logging is permitted in Cameroon’s FMUs, which are further divided into logging concessions called annual allowable cuts (AACs). These logging permits require owners and operators to maintain permanent forest cover. This data set was produced in collaboration between the Cameroon Ministry of Forestry and Wildlife and WRI. For more information and data sets, see the <a href='http://www.wri.org/project/interactive-forest-atlas-cameroon' target='_blank'>Interactive Forest Atlas for Cameroon</a>.</p>

              <p class='credits'><strong>Credit:</strong> Cameroon Ministry of Forestry and Wildlife, World Resources Institute.</p>
            </div>

            <div id="canada_forest_tenures" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/Original/Canada_forest_tenures_2013.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries of forest areas licensed to companies for forestry and is a compilation of provincial forest tenure data sets across Canada. Tenures for Nova Scotia are not provided, due to changes underway in forest management in that province, and will be added to this data set when available. Much of Canada’s logging activity occurs on Crown (often referred to as “public”) land and is regulated by various provincial commercial forest tenure systems that allocate cutting rights to and confer obligations on recipients of the tenures. It is these tenure systems on Crown forest land that are the focus of this data product. The information in this data set was gathered in order to continue to develop an understanding of how much of Canada’s forest area is under commercial forest tenures, where these tenures (including the operating areas of major forest companies) are located, and who is most likely to control them. This information and these data sets are important because of the extent of tenures and the resulting logging activity over vast areas of Canada’s Crown forest land.</p>

              <p class='credits'><strong>Credit:</strong> Global Forest Watch Canada, 2013.</p>
            </div>

            <div id="central_african_republic_logging_permits" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Central_African_Republic/GIS_Data/CAF_permits_exploitation_2013.zip' target='_blank'>Download</a></p>

              <p>This data set provides the permit boundaries for selective logging in the Central African Republic’s production forests. This data set was produced through a collaboration between the CAR Ministry of Water, Forests, Hunting, and Fishing (MEFCP) and WRI. For more information, see the <a href='http://www.wri.org/publication/interactive-forest-atlas-central-african-republic' target='_blank'>Interactive Forest Atlas for the Central African Republic</a>.</p>

              <p class='credits'><strong>Credit:</strong> Central African Republic Ministry of Water and Forests, Hunting, and Fishing; German Technical Cooperation (GIZ); French Development Agency (AFD); Special Allocation Fund for Forest Development (CASDF); World Resources Institute</p>
            </div>

            <div id="democratic_republic_of_the_congo_forest_titles" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Democratic_Republic_Congo/GIS_Data/DRC_affectation_territoriale_2013.zip'>Download</a></p>

              <p>Forest concession data for the Democratic Republic of the Congo (DRC) represent geographic areas permitted for exploitation of timber by selective logging. This data set was produced through a collaboration between the DRC Ministry of Environment, Nature Conservation, and Tourism (MECNT) and WRI. For more information, see the <a href='http://www.wri.org/publication/interactive-forest-atlas-democratic-republic-of-congo' target='_blank'>Interactive Forest Atlas for the Democratic Republic of the Congo</a></p>

              <p class='credits'><strong>Credit:</strong> Democratic Republic of the Congo Ministry of Environment, Nature Conservation, and Tourism (MECNT); Department for Permanent Service for Forest Inventory and Management (SPIAF); World Resources Institute; Asset Management and Forest Management (DIAF); Forest Management Branch (FMB)</p>
            </div>

            <div id="equatorial_guinea_logging_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Equatorial_Guinea/GIS_Data/Parcelas_forestales.zip'>Download</a></p>

              <p>This data set provides the boundaries for logging concessions for Equatorial Guinea, where commercial forestry is permitted. The Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute produced this information in a collaboration for the Interactive Forest Atlas of Equatorial Guinea. Information provided in the data set includes the boundaries of concessions, the operating company, the ownership group, documentation on the permit process, and other information.</p>

              <p class='credits'><strong>Credit:</strong> Equatorial Guinea Ministry of Agriculture and Forests and the World Resources Institute, 2013</p>
            </div>

            <div id="gabon_forest_licenses" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Gabon/GIS_Data/Amenagement_forestier.zip'>Download</a></p>

              <p>This data set provides the boundaries for forest licenses in Gabon. The licenses cover areas permitted for selective logging. This data set was produced in a collaboration between the Gabon Ministry of Forest Economy, Water, Fisheries, and Aquaculture (MEFEPA) and WRI. For more information, see the Interactive Forest Atlas for Gabon.</p>

              <p class='credits'><strong>Credit:</strong> Gabon Ministry of Forest Economy, Water, Fisheries, and Aquaculture (MEFEPA); World Resources Institute</p>
            </div>

            <div id="indonesia_logging_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/Original/Indonesia_logging_2010_wgs84.zip'>Download</a></p>
              <p>This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of logging concessions for the selective logging of natural forests in Indonesia. According to this data set, there are 557 active logging concessions in Indonesia.</p>

              <p class='credits'><strong>Credit:</strong> Indonesia Ministry of Forestry</p>
            </div>

            <div id="liberia_logging_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/gfw2_lbr_lc.zip' target='_blank'>Download</a></p>

              <p>The Liberia logging concessions data set combines the boundaries of forest management contracts, timber sale contracts, and private use permits, compiled by Global Witness from available government and contractual maps. Due to a lack of quality data, this data set should be used for demonstration purposes only and not for land use planning purposes. Forests in Liberia are managed by the state under the National Forestry Reform Law, enacted in October 2006. Following the enactment of this law, UN Security Council timber export sanctions against Liberia were lifted, permitting commercial forestry activities to resume. Forest management contracts include concession areas of 50,000 hectares to 400,000 hectares and are open for bids from qualified bidders that demonstrate at least 51% ownership by Liberian citizens; concessions of more than 100,000 hectares are open for bidding from international investors. Timber sale contracts are established through a bidding process for areas up to 5,000 hectares; bidders must demonstrate at least 51% ownership by Liberian citizens. Private Use Permits are licenses issued to private landowners to extract wood and lack sustainability regulations. Global Witness compiled the logging concession data as part of the report <em>Signing Their Lives Away: Liberia’s Private Use Permits and the Destruction of Community Owned Rainforest</em> (Global Witness, 2012).</p>

              <p class='credits'><strong>Credit:</strong> Global Witness, 2012</p>
            </div>

            <div id="republic_of_the_congo_logging_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Congo/GIS_Data/COG_affectation_territoriale_2013.zip'>Download</a></p>

              <p>Forest concession data for the Republic of the Congo provide the boundaries of areas permitted for selective logging. These areas, also called Forest Exploitation Units, are managed in accordance with the national Forest Code. This data set was produced through a collaboration between the Republic of the Congo Ministry of Forest Economy and WRI. For more information, see the <a href='http://www.wri.org/project/interactive-forest-atlas-congo' target='_blank'>Interactive Forest Atlas for the Republic of the Congo</a>.</p>

              <p class='credits'><strong>Credit:</strong> Republic of the Congo Ministry of Forest Economy (MEF), National Center for Inventory and Planning of Forest and Wildlife Resources (CNIAF), World Resources Institute</p>
            </div>
          </div>
        </div>
      </div>
    </li>

    <li id='mining' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Mining</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://wri-public-data.s3.amazonaws.com/gfw2/GFW_mining_20140218.zip' target='_blank' title='Download'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by governments to companies for extraction of minerals</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Cambodia, Cameroon, Canada, Colombia, Democratic Republic of the Congo (DRC), Gabon, and Republic of the Congo</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of government documents, satellite imagery, and GPS data. <span class='hidden'> For information on country-specific concessions data please refer to the <%= link_to 'Data page', "#{sources_path}#forest_use" %>.</span></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Mining concession” refers to an area allocated by a government or other body for the extraction of minerals. The terminology for these areas varies from country to country.</p>

          <p>The mining concession data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>

          <p>Mining concession data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. <span class='hidden'>See the <%= link_to 'Data page', "#{sources_path}#forest_use" %> for details on specific data sets.</span></p>

          <p>If you are aware of concession data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Mining.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org/' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently includes mining concession data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_mining">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_mining'>
              <ul>
                <li><a href='#' data-slug="canada_mining_concessions">Canada Mining Concessions</a></li>
                <li><a href='#' data-slug="cameroon_mining_permits">Cameroon mining permits</a></li>
                <li><a href='#' data-slug="colombia_mining_titles">Colombia mining titles</a></li>
                <li><a href='#' data-slug="democratic_republic_of_the_congo_mining_permits">Democratic Republic of the Congo mining permits</a></li>
                <li><a href='#' data-slug="gabon_mining_permits">Gabon mining permits</a></li>
                <li><a href='#' data-slug="republic_of_the_congo_mining_permits">Republic of the Congo mining permits</a></li>
              </ul>
            </div>
            <div id="canada_mining_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://www.globalforestwatch.ca/node/201' target='_blank'>Download</a></p>

              <p>This dataset provides the boundaries for the following industrial concessions across Canada: active mineral prospecting permits across the Northwest Territories and Nunavut as of July 2012; Active mineral leases as of February 2013; Active mineral claims as of February 2013. Active coal tenures as of February 2013. Data was obtained from provincial and federal government websites and may be subject to use limitations listed with those sources. This data compilation was completed by Global Forest Watch Canada.</p>

              <p class='credits'><strong>Credit:</strong> Global Forest Watch Canada.</p>
            </div>
            <div id="cameroon_mining_permits" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Cameroon/GIS_Data/CMR_mining_2013.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries of prospective mining permits for Cameroon, as well as the type of mineral resource. Map data were provided by the Cameroon Ministry of Mines and Technological Development and published in a collaboration with the World Resources Institute. For more information and data sets, see the <a href='http://www.wri.org/project/interactive-forest-atlas-cameroon' target='_blank'>Interactive Forest Atlas for Cameroon</a>.</p>

              <p class='credits'><strong>Credit:</strong> Cameroon Ministry of Mines and Technological Development.</p>
            </div>

            <div id="colombia_mining_titles" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/Colombia_Mining_Permits_201401.zip' target="_blank">Download</a></p>

              <p>This data set provides the boundaries of mining titles (<em>títulos mineros concedidos</em>) for Colombia. The shapefiles are compiled by Tierra Minada, a Colombian civil society group, utilizing information from the Colombian Mining Registry, which is maintained by the National Mining Agency. For more information about the data sets, visit the <a href="https://sites.google.com/site/tierraminada/" target="_blank">Tierra Minada website</a> or <a href="http://www.simco.gov.co/Inicio/CatastroMineroColombiano/tabid/107/Default.aspx" target="_blank">Colombia’s Mining Cadaster Portal</a>.</p>

              <p class="credits"><strong>Credit:</strong> Tierra Minada; Agencia Nacional de Minería de Colombia.</p>
            </div>

            <div id="democratic_republic_of_the_congo_mining_permits" class="source_dropdown_body">
              <p>This data set provides the boundaries for mining permits in the Democratic Republic of the Congo. This data set is available from the Ministry of Mines Mining Registry (CAMI), for purchase and could not be made available for public download. For more information, see the <a href='http://portals.flexicadastre.com/drc/en/'>DRC's mining cadaster portal</a>.</p>

              <p class='credits'><strong>Credit:</strong> Democratic Republic of the Congo Ministry of Mines Mining Registry (CAMI)</p>
            </div>

            <div id="gabon_mining_permits" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/gfw2_gab_mc.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries for mining permits and prospecting permits for Gabon. The data set was produced by the Gabon Ministry of Mines, Petroleum, and Hydrocarbons and the World Resources Institute for the Interactive Mining, Forest, and Conservation Atlas of Gabon. For more information, see the <a href='http://www.wri.org/publication/interactive-forestry-atlas-gabon' target='_blank'>Interactive Forest Atlas of Gabon</a>.</p>

              <p class='credits'><strong>Credit:</strong> Gabon Ministry of Mines, Petroleum, and Hydrocarbons; World Resources Institute.</p>
            </div>

            <div id="republic_of_the_congo_mining_permits" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Congo/GIS_Data/COG_permis_minier_2011.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries of mining permits for the Republic of the Congo. The original map data were produced by the Republic of the Congo Ministry of Mines and Geology with the support of the World Resources Institute. For more information, see the <a href='http://www.wri.org/project/interactive-forest-atlas-congo' target='_blank'>Interactive Forest Atlas for the Republic of the Congo</a>.</p>

              <p class='credits'><strong>Credit:</strong> Republic of the Congo Ministry of Mines and Geology, World Resources Institute.</p>
            </div>
          </div>
        </div>
      </div>
    </li>

    <li id='oil_palm' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Oil palm</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://wri-public-data.s3.amazonaws.com/gfw2/GFW_oilpalm_20131023.zip' target='_blank' title='Download'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by governments to companies for oil palm plantations</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Cameroon, Republic of the Congo, Indonesia, and Liberia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of government documents, satellite imagery, and GPS data. <span class='hidden'> For information on country-specific concessions data please refer to the <%= link_to 'Data page', "#{sources_path}#forest_use" %>.</span></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Oil palm concession” refers to an area allocated by a government or other body for industrial-scale oil palm plantations.</p>

          <p>The oil palm concession data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>

          <p>Oil palm concession data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. <span class='hidden'>See the <%= link_to 'Data page', "#{sources_path}#forest_use" %> for details on specific data sets.</span></p>

          <p>If you are aware of concession data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Oil Palm.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org/' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently includes oil palm concession data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_oil_palm">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_oil_palm'>
              <ul>
                <li><a href='#' data-slug="cameroon_agro_industrial_zones">Cameroon agro-industrial zones</a></li>
                <li><a href='#' data-slug="indonesia_oil_palm_concessions">Indonesia oil palm concessions</a></li>
                <li><a href='#' data-slug="liberia_oil_palm_concessions">Liberia oil palm concessions</a></li>
                <li><a href='#' data-slug="republic_of_the_congo_oil_palm_concessions">Republic of the Congo oil palm concessions</a></li>
              </ul>
            </div>

            <div id="cameroon_agro_industrial_zones" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/Forest_Atlas/Cameroon/GIS_Data/CMR_agric_zone_2013.zip' target='_blank'>Download</a></p>

              <p>This data layer shows the boundaries of agro-industrial zones, where oil palm and rubber tree plantations, as well as other crops, may be established. In Cameroon, industrial agriculture falls outside of the National Forest Estate. Agricultural plantations are allocated by the Ministry of Economy and Planning to private entities under long-term, renewable contracts, which are then monitored by the Ministry of Agriculture. The agro-industrial data set was mapped using satellite imagery, with ground-truthing to determine the crop type and operating company. Official documentation was often lacking, so boundaries should be considered approximate and nonexhaustive.</p>

              <p class='credits'><strong>Credit:</strong> World Resources Institute.</p>
            </div>

            <div id="indonesia_oil_palm_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/Original/Indonesia_oilpalm_2010_wgs84.zip' target='_blank'>Download</a></p>
              <p>This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned oil palm plantations in Indonesia. This data set is known to be incomplete, but it is currently the best available.</p>

              <p class='credits'><strong>Credit:</strong> Indonesia Ministry of Forestry.</p>
            </div>

            <div id="liberia_oil_palm_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/gfw2_lbr_op.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries of known oil palm plantations for Liberia and was compiled by Global Witness from available government maps. Information provided with this data set includes company, ownership group, and land area.</p>

              <p class='credits'><strong>Credit:</strong> Global Witness, 2013.</p>
            </div>

            <div id="republic_of_the_congo_oil_palm_concessions" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/gfw2_cgo_op.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries for oil palm plantations according to the Republic of the Congo Ministry of Agriculture. </p>

              <p class='credits'><strong>Credit:</strong> World Resources Institute; Republic of the Congo Ministry of Agriculture.</p>
            </div>
          </div>
        </div>
      </div>
    </li>

    <li id='wood_fiber_plantations' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Wood Fiber Plantations</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://wri-public-data.s3.amazonaws.com/gfw2/GFW_woodfiber_20130402.zip' target='_blank' title='Download'>Download<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by governments to private companies for tree plantations for production of timber and wood pulp for paper and paper products</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Republic of the Congo and Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of government documents, satellite imagery, and GPS data. <span class='hidden'> For information on country-specific concessions data please refer to the <%= link_to 'Data page', "#{sources_path}#forest_use" %>.</span></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not include all existing concessions in a country, and the location of certain concessions can be inaccurate.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Wood fiber concession” refers to an area allocated by a government or other body for establishment of fast-growing tree plantations for the production of timber and wood pulp for paper and paper products.</p>

          <p>The wood fiber concession data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>

          <p>Wood fiber concession data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. <span class='hidden'>See the <%= link_to 'Data page', "#{sources_path}#forest_use" %> for details on specific data sets.</span></p>

          <p>If you are aware of concession data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Wood Fiber.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org/' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently hosts wood fiber concession data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_wood_fiber_plantations">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_wood_fiber_plantations'>
              <ul>
                <li><a href='#' data-slug="indonesia_wood_fiber_plantations">Indonesia wood fiber plantations</a></li>
                <li><a href='#' data-slug="republic_of_the_congo_eucalyptus_plantations">Republic of the Congo eucalyptus plantations</a></li>
              </ul>
            </div>

            <div id="indonesia_wood_fiber_plantations" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/Original/Indonesia_timber_2010_wgs84.zip' target='_blank'>Download</a></p>

              <p>This data set, produced by the Indonesia Ministry of Forestry, provides the boundaries of current or planned wood fiber plantations in Indonesia. This data set is known to be incomplete, but it is compiled from the best information currently available.</p>

              <p class='credits'><strong>Credit:</strong> Indonesia Ministry of Forestry.</p>
            </div>

            <div id="republic_of_the_congo_eucalyptus_plantations" class="source_dropdown_body">
              <p class='download'><a href='http://wri-public-data.s3.amazonaws.com/gfw2/gfw2_cgo_wf.zip' target='_blank'>Download</a></p>

              <p>This data set provides the boundaries for eucalyptus and other plantations in the Republic of the Congo. The World Resources Institute compiled information from the Republic of the Congo Ministry of Agriculture to produce this data layer.</p>

              <p class='credits'><strong>Credit:</strong> World Resources Institute; Republic of the Congo Ministry of Agriculture.</p>
            </div>
          </div>
        </div>
      </div>
    </li>


    <li id='concesiones_forestales' class='source-item last'>
      <div class='source_header'>
        <strong class='source_title'>Peru forest concessions</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by the Peruvian Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, ecotourism, conservation, reforestation and afforestation activities.</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Peru</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Source data</dt>
            <dd>Organismo de Supervisión de los Recursos Forestales y de Fauna Silvestre (OSINFOR)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Date of content</dt>
            <dd>Last updated September 2014</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>Displays boundaries of areas allocated by the Peruvian Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, ecotourism, conservation, reforestation and afforestation. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. </p>
          <p>The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted. Types of concessions are indicated in the attribute data of each boundary, and include:</p>
          <ul class="bullets">
            <li>Timber Concessions</li>
            <li>Non-Timber Forest Products</li>
            <li>Ecotourism Concessions</li>
            <li>Conservation Concessions</li>
            <li>Wildlife Concessions</li>
            <li>Reforestation/Afforestation areas</li>
          </ul>

          <p class='credits'><strong>Citation:</strong> For more information on each of the concession types, please visit the <a href='http://www.osinfor.gob.pe/portal/documentos.php?idcat=5&idaso=5' target='_blank'>OSINFOR website</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>
      </div>
    </li>
    <li id='concesiones_forestalesNS' class='source-item last'>
      <div class='source_header'>
        <strong class='source_title'>Peru forest concessions</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas allocated by the Peruvian Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, ecotourism, conservation, reforestation and afforestation activities.</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Peru</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Source data</dt>
            <dd>Organismo de Supervisión de los Recursos Forestales y de Fauna Silvestre (OSINFOR)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Date of content</dt>
            <dd>Last updated September 2014</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>Displays boundaries of areas allocated by the Peruvian Supervisory Body for Forest and Wildlife Resources (OSINFOR) for timber, non-timber products, ecotourism, conservation, reforestation and afforestation. The concession grants the licensee the exclusive right to the sustainable use of natural resources granted under the conditions and limitations established by the respective title. </p>
          <p>The concession grants the holder the right to use and enjoy the natural resource granted and, consequently, ownership of the fruits and products extracted. Types of concessions are indicated in the attribute data of each boundary, and include:</p>
          <ul class="bullets">
            <li>Timber Concessions</li>
            <li>Non-Timber Forest Products</li>
            <li>Ecotourism Concessions</li>
            <li>Conservation Concessions</li>
            <li>Wildlife Concessions</li>
            <li>Reforestation/Afforestation areas</li>
          </ul>

          <p class='credits'><strong>Citation:</strong> For more information on each of the concession types, please visit the <a href='http://www.osinfor.gob.pe/portal/documentos.php?idcat=5&idaso=5' target='_blank'>OSINFOR website</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}#forest_use" %></em></p>
        </div>
      </div>
    </li>


    <li id='rspo-concessions' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>RSPO CONCESSIONS</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of RSPO Certified Mill production areas of RSPO member companies</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Variable</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global coverage for wherever RSPO certified areas exist. Currently, certified areas exist in: Indonesia, Malaysia, Papua New Guinea, and Brazil.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>oundtable on Sustainable Palm Oil (RSPO) Member Companies, Aidenvironment</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>TBD</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>May 2013</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>These data are created from multiple sources and are for illustrative purposes only. RSPO/ASI accredited certification bodies (CBs) are required to present maps and GPS coordinates in their public summary reports as stipulated in Annex 4 of the RSPO Certification Systems document. However, there are no qualifications as to what information these maps should show. As a result, the quality of maps presented is highly variable and generally poor. Source data and boundaries depicted have not been verified in detail.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Disclaimer</dt>
            <dd><a href="http://d1ooe4m5rq52vq.cloudfront.net/downloads/DisclaimerforRSPOMapPublication_FINAL.pdf" target="_blank">Data disclaimer</a></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>The RSPO Certified Mills data layer displays plantation management units (PMU) certified under RSPO through May, 2013. These data are arranged by company group holding the majority shareholding in each unit.</p>
          <p>The RSPO concession boundaries were produced by member companies and compiled by Aidenvironment. Other data is included from Annual Surveillance Assessments provided by the RSPO as well as third-party audit reports screened by Aidenvironment</p>
        </div>
      </div>
    </li>
    <li id='palm-oil-mills' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>PALM OIL MILLS</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays palm oil mills certified to be sustainable by the Roundtable for Sustainable Palm Oil.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Roundtable for Sustainable Palm Oil</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>May 2014</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>Mills certified after May 2014 are not included in the data. Certification is granted to a mill and its supply base, and the current data set does not reflect any grievances or non-compliance issues with the mill or supplying concession areas. The data does not include mills that may produce according to RSPO standards but have yet to receive certification.</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Certification of sustainable palm oil production is handled through the palm oil mill and its supply base. The certification includes plantations managed by the mill and plantations managed by other suppliers, including smallholders. To have its oil mill certified, a palm oil producer must show a plan to have 100% of its associated smallholders meet RSPO standards within 3 years. Oil mills and their supply base hire an RSPO-approved third party certification body to set up audits testing their compliance with the RSPO Principles and Criteria.</p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id='conservation' class='conservation source-article'>
  <h2 class='source_category_title'>Conservation</h2>
  <p class='source_category_description'>Conservation data measure information that is important for preservation and enhancement of protected areas and natural resources such as biodiversity.</p>

  <ul class='sources'>
    <li id='protected_areas' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Protected Areas</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.protectedplanet.net' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays areas that are legally protected according to various designations (e.g., national parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>The World Database on Protected Areas, which compiles protected area data from governments, NGOs, and international secretariats</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Monthly</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by protected area</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Protected area designations, such as “National Park,” can be applied differently in different countries. Therefore, the associated IUCN category and its description of protection may also vary by country.</p>
              <p>Protected areas with no boundary data are displayed as brown dotted boxes, which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set on marine and terrestrial protected areas available. Protected area data are provided via <a href='http://www.protectedplanet.net/' target='_blank'>protectedplanet.net</a>, the online interface for the <a href='http://www.wdpa.org/' target='_blank'>World Database on Protected Areas</a> (WDPA). The WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced information about protected areas.</p>

          <p><strong><a href='http://www.iucn.org/about/work/programmes/gpap_home/gpap_quality/gpap_pacategories/' target='_blank'>IUCN Management Categories</a></strong></p>

          <p>Not all protected areas receive the same degree of protection. While some have strict guidelines designed to preserve intact ecosystems, others allow for sustainable land use, often including limited resource extraction. In addition, not all countries use the same terminology when designating a protected area. Accordingly, the  <a href='http://www.iucn.org/' target='_blank'>International Union for Conservation of Nature</a> defined universal management categories that stipulate the level of protection for most protected areas.</p>

          <p>As you click through protected areas in this layer, note the “legal designation” and the explanations below to better understand the degree to which an area is protected.</p>

          <ul class='bullets'>
            <li><strong>Ia. Strict Nature Reserves.</strong> Protected areas designed to preserve biodiversity and all geological features. Limited human use (e.g., scientific study, education) is allowed and carefully monitored. Strict Nature Reserves are often used to understand the impact of indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of preservation. <em>Other common designations:</em> Biological Reserve, Botanical Reserve</li>
            <li><strong>Ib. Wilderness Areas.</strong> Protected areas managed to preserve ecosystem processes with limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s center), but they allow for local indigenous groups to maintain subsistence lifestyles. These areas are often established to restore disturbed environments. <em>Other common designations:</em> Wilderness Reserve, Wildlife Area</li>
            <li><strong>II. National Parks.</strong> Protected areas designed to preserve large-scale ecosystems and support human visitation. With conservation as a priority, these areas allow infrastructure and contribute to the local economy by providing opportunities for environmental educational and recreation. <em>Other common designations:</em> State Park, Class A Park, Park Reserve, Provincial Park</li>
            <li><strong>III. National Monuments or Features.</strong> Areas established to protect a specific natural feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or environmental importance and the immediate surroundings. Accordingly, Natural Monuments or Features are typically smaller in area and have high human impact resulting from visitor traffic. <em>Other common designations:</em> Natural Features Reserve, Nature Monument, Botanical Garden</li>
            <li><strong>IV. Habitat and Species Management Areas.</strong> Areas designed to conserve specific wildlife populations and/or habitats. Habitat and Species Management Areas often exist within a larger ecosystem or protected area and are carefully managed (e.g., through hunting abatement or habitat restoration) to conserve a target species or habitat. <em>Other common designations:</em> National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve, Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary</li>
            <li><strong>V. Protected Landscapes and Seascapes.</strong> Protected areas with ecological, biological, or cultural importance that have been shaped by human use of the landscape. Protected landscapes and seascapes typically cover entire bodies of land or ocean and allow for a number of for-profit activities (e.g., ecotourism) in accordance with the region’s management plan. <em>Other common designations:</em> National Forest, State Natural Area, Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature Reserve, State Natural Area</li>
            <li><strong>VI. Protected Areas with Sustainable Use of Natural Resources.</strong> Areas designed to manage natural resources and uphold the livelihoods of surrounding communities. These regions have a low level of human occupation, small-scale developments (i.e., not industrial), and part of the landscape in its natural condition. <em>Other common designations:</em> Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest, Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve</li>
          </ul>

          <h3 class='overview_title'>Other Important Designations</h3>
          <ul class='bullets'>
            <li><a href='http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/' target='_blank'>UNESCO-MAP Biosphere Reserves</a>: areas under UNESCO’s Man and the Biosphere Programme designated to “promote sustainable development based on local community efforts and sound science.”</li>
            <li><a href='http://whc.unesco.org/' target='_blank'>World Heritage Sites</a>: areas considered to have “outstanding universal value” and meet at least one of ten criteria, as described <a href='http://whc.unesco.org/en/criteria/' target='_blank'>here</a>.</li>
            <li><a href='http://ramsar.org/cda/en/ramsar-home/main/ramsar/1_4000_0__' target='_blank'>Ramsar Sites—Wetlands of International Importance</a>: wetlands that hold significant value designated under the Ramsar Convention on Wetlands.</li>
          </ul>

           <p class='credits'><strong>Citation:</strong> UNEP-WCMC, UNEP, and IUCN. “World Database on Protected Areas.” Accessed on [date]. <a href='http://www.protectedplanet.net/' target='_blank'>www.protectedplanet.net</a>.</p>

           <p class='read_more hidden'><em><a href='http://www.protectedplanet.net/' target='_blank'>Learn more or download data at www.protectedplanet.net</a></em></p>
        </div>
      </div>
    </li>

    <li id='biodiversity_hotspots' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Biodiversity hotspots</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.conservation.org/where/priority_areas/hotspots/Pages/hotspots_main.aspx' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays Conservation International’s biodiversity hotspots—defined regions around the world where biodiversity conservation is most urgent because of high levels of endemism and human threat </dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global (land only)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Updated as new data becomes available through Conservation International</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2011</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>This layer only displays the land-based portion of biodiversity hotspots, although some hotspots extend offshore</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>First defined in 1988 by scientist Norman Myers, biodiversity hotspots are areas characterized by high levels of endemic plants coupled with significant habitat loss. Specifically, a region must meet the following criteria to achieve Conservation International’s hotspot classification:</p>

          <ul class='bullets'>
            <li>At least 1,500 species of vascular plants (>0.5% of the world’s total) are endemic</li>
            <li>At least 70% of the original natural vegetation has been lost</li>
          </ul>

          <p>When Myers first defined the term, he identified 10 tropical forest hotspots. The need to pinpoint priority conservation regions led Conservation International (CI) to adopt the term and reassess the hotspot concept. In this process, CI introduced quantitative thresholds (see above) and added additional regions. At that time, there were 25 hotspots. Because of the constant change in environmental threats and the improved understanding of biodiversity, CI has since revisited the hotspots to refine boundaries, update information, and add new regions. This process produced an additional 10 hotspots, bringing the total to 35.</p>

          <p class='credits'><strong>Citation:</strong> Conservation International. “Biodiversity Hotspots.” Accessed on [date]. <a href='http://www.conservation.org/where/priority_areas/hotspots/Pages/hotspots_main.aspx' target='_blank'>www.conservation.org/where/priority_areas/hotspots/Pages/hotspots_main.aspx</a>.</p>

          <p class='read_more hidden'><em><a href='http://www.conservation.org/where/priority_areas/hotspots/Pages/hotspots_main.aspx' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>
    </li>
    <li id='wwf' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>WWF Terrestrial Ecoregions of the World</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://assets.worldwildlife.org/publications/15/files/original/official_teow.zip?1349272619' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays large-scale areas containing a distinct assemblage of biological communities shaped by similar species, dynamics and environmental conditions</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>-</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2004</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd><p>No single biogeographic framework is optimal for all taxa. Ecoregions reflect the best compromise for as many taxa as possible.</p><p>Ecoregion boundaries rarely form abrupt edges; rather, ecotones and mosaic habitats bound them.</p><p>Most ecoregions contain habitats that differ from their assigned biome (e.g., for example, rainforest ecoregions in Amazonia often contain small edaphic savannas).</p></dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>Terrestrial Ecoregions of the World (TEOW) is a data set of the geographic distribution Earth's terrestrial biodiversity. Ecoregions are defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, environmental conditions, and processes, such as migrations or fire disturbance regimes. The 867 terrestrial ecoregions are classified into 14 different biomes, such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities.</p>
          <p>The TEOW data set provides:</p>

          <ul class='bullets'>
            <li>A map of terrestrial biodiversity that gives enough detail to be useful in global and regional conservation priority-setting and planning efforts</li>
            <li>A logical biogeographic framework for developing large-scale conservation strategies</li>
            <li>A framework for a global species database useful in priority setting and ecological analyses</li>
            <li>A foundation for the <a href="http://www.worldwildlife.org/publications/global-200">Global 200</a>, a representative prioritization of the world’s most distinctive biodiversity regions</li>
          </ul>

          <p>The TEOW data set was developed by WWF through primary analysis, secondary research, and consultation of existing biogeographic maps and regional experts.</p>

          <p class='credits'><strong>Citation:</strong> Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</p>

          <p class='read_more hidden'><em><a href='http://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>
    </li>
    <li id='birdlife' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Endemic Bird Areas</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='https://www.dropbox.com/s/1171xuj56jhauoi/EBA.zip?dl=0' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays areas where the geographic range of two or more endemic bird species overlaps</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>BirdLife International</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Updated Annually</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2014</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>While many bird species are widespread, over 2,500 are endemic and restricted to an area smaller than 5 million hectares (restricted-range species). BirdLife International has mapped every restricted-range species using geo-referenced locality records. Through this process, they identified regions of the world—known as “Endemic Bird Areas” (EBAs)—where the distributions of two or more of these species overlap.</p>

          <p>Half of all restricted-range species are globally threatened or near-threatened, and the other half remain vulnerable to loss or degradation of habitat. The majority of EBAs are also important for the conservation of restricted-range species from other animal and plant groups. The unique landscapes where these bird species occur, amounting to just 4.5% of the earth's land surface, are high priorities for broad-scale ecosystem conservation.</p>

          <p>Geographically, EBAs are often islands or mountain ranges, and vary considerably in size, from a few hundred hectares to more than 10,000,000 hectares. EBAs also vary in the number of restricted-range species that they support (from two to 80). EBAs are found around the world, but most (77%) of them are located in the tropics and subtropics.</p>

          <p class='credits'><strong>Citation:</strong>Stattersfield, A.J., Crosby, M.J., Long, A.J. and Wege, D.C. (1998) Endemic Bird Areas of the World. Priorities for biodiversity conservation. BirdLife Conservation Series 7. Cambridge: BirdLife International.</p>

          <p class='read_more hidden'><em><a href='http://www.birdlife.org/datazone/eba' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>
    </li>
    <li id='azepoly' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Alliance for Zero Extinction sites</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://gfw2-data.s3.amazonaws.com/conservation/zip/tiger_conservation_landscapes.zip' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays critical sites for conservation that contain endangered species with limited ranges and populations found nowhere else on the planet</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>The Alliance for Zero Extinction</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Every 5 years</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2010; Updated March 16, 2012</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>Created by the <a href="http://www.zeroextinction.org/">Alliance for Zero Extinction (AZE)</a>, this data set shows 587 sites for 920 species of mammals, birds, amphibians, reptiles, conifers, and reef-building corals. The species found within these sites have extremely small global ranges and populations; any change to habitat within a site may lead to the extinction of a species in the wild. To meet AZE Extinction Site status, a site must:</p>
          <ul class='bullets'>
            <li>Contain at least one Endangered or Critically Endangered species</li>
            <li>Be the sole area where an Endangered or Critically Endangered species occurs</li>
            <li>Contain greater than 95% of either the known resident population of the species or 95% of the known population of one life history segment (e.g. breeding or wintering) of the species</li>
            <li>Have a definable boundary (e.g., species range, extent of contiguous habitat, etc.)</li>
          </ul>

          <p>Launched in 2005, the Alliance for Zero Extinction (AZE) engages 83 non-governmental biodiversity conservation organizations working to prevent species extinctions. The AZE identifies and safeguards places where species evaluated to be Endangered or Critically Endangered by the <a href="http://
            www.iucn.org">International Union for Conservation of Nature</a> are restricted to single remaining sites.</p>
          <p class='credits'><strong>Citation:</strong>Alliance for Zero Extinction (2010). 2010 AZE Update. <a href="http://www.zeroextinction.org">www.zeroextinction.org</a>.</p>

          <p class='read_more hidden'><em><a href='http://www.birdlife.org/datazone/eba' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>
    </li>
    <li id='tigers' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Tiger Conservation Landscapes</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://gfw2-data.s3.amazonaws.com/conservation/zip/tiger_conservation_landscapes.zip' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays contiguous areas of suitable tiger habitat that have shown consistent and confirmed evidence of tigers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Bangladesh, Bhutan, Cambodia, China, India, Indonesia, Lao PDR, Malaysia, Myanmar, Nepal, Russia, Thailand and Vietnam.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Dinerstein et al. 2007</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Updated as new data becomes available through Biodiversity & Wildlife Solutions, RESOLVE</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2007</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Tiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.<p></p>Tigers require a large area to survive. Accordingly, habitat loss is a major cause of the species’ rapid decline. Before this data set, many countries containing tigers did not have spatially explicit tiger habitat maps necessary to develop habitat conservation and management plans. Among others, this information gap was an impetus to developing the TCL data set.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>Tiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.</p><p>Tigers require a large area to survive. Accordingly, habitat loss is a major cause of the species’ rapid decline. Before this data set, many countries containing tigers did not have spatially explicit tiger habitat maps necessary to develop habitat conservation and management plans. Among others, this information gap was an impetus to developing the TCL data set.</p>

          <p class='credits'><strong>Credits:</strong> Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007. “The fate of wild tigers.” BioScience 57 (June 2007): 508-14.</p>

          <p class='read_more hidden'><em><a href='http://data.globalforestwatch.org/datasets/04d892c083f54c638228931da081467b_4?filterByExtent=true' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>

      <div class='source_header'>
        <strong class='source_title'>Tx2 Tiger Conservation Landscapes</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://gfw2-data.s3.amazonaws.com/conservation/zip/tiger_conservation_landscapes_tx2.zip' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays areas that could double the wild tiger population through proper conservation and management by 2020</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Bangladesh, Bhutan, Cambodia, China, India, Indonesia, Lao PDR, Malaysia, Myanmar, Nepal, Russia, Thailand and Vietnam.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Wikramanayake et al. 2011</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Updated annually through Biodiversity & Wildlife Solutions, RESOLVE</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2011</dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set displays 29 Tx2 Tiger Conservation Landscapes (Tx2 TCLs), defined areas that could double the wild tiger population through proper conservation and management by 2020.</p>
          <p>The number of wild tigers has declined from an estimated 100,000 in the early 1900s to a current estimate of around 3,500 adult animals. In response to this rapid decline, government officials convened in November 2010 to endorse the St. Petersburg Declaration, pledging to double the wild tiger population by 2020. To aid in this effort, Wikramanayake and his team conducted a landscape analysis of tiger habitat to determine if a recovery of such magnitude is possible. They identified 29 Tiger Conservation Landscapes with potential for doubling wild tiger population with proper conservation and management.</p>

          <p class='credits'><strong>Credits:</strong> Wikramanayake, E., Dinerstein, E., Seidensticker, J., Lumpkin, S., Pandav, B., Shrestha, M., Mishra, H., Ballou, J., Johnsingh, A.J.T., Chestin, I., Sunarto, S., Thinley, P., Thapa, K., Jiang, G., Elagupillay, S., Kafley, H., Pradhan, N.M.B., Jigme, K., Teak, S., Cutter, P., Aziz, Md. A., Than, U. 2011. A landscape-based conservation strategy to double the wild tiger population. Conservation Letters, 4 (3):219-227.</p>
        </div>
      </div>

      <div class='source_header'>
        <strong class='source_title'>Terai Arc Landscape Corridors</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://gfw2-data.s3.amazonaws.com/conservation/zip/critical_tiger_corridors.zip' target='_blank' title='Download'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays critical forest corridors connecting existing tiger populations in Nepal and India.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Nepal and India.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Terai Arc Landscape project</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Updated annually through Biodiversity & Wildlife Solutions, RESOLVE</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2014</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>The corridors are existing forests or grasslands which might also include human settlements.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>This data set displays 9 forest corridors on the Nepalese side of the <a href="http://wwf.panda.org/what_we_do/how_we_work/conservation/species_programme/species_people/our_solutions/tal_nepal/">Terai Arc Landscape (TAL)</a>.  Corridors are defined as existing forests connecting current Royal Bengal tiger meta-populations in Nepal and India.</p>
          <p>The TAL is spread over 4.95 million hectares, linking 14 transboundary protected areas across Nepal and India. This landscape has the second largest population of rhinos, one of the highest densities of tiger populations, and is home to the Asiatic elephant. In Nepal, TAL encompasses 2.31 million hectares extending over 14 districts and includes 75 percent of the remaining forests of lowland Nepal.  In addition, TAL was recognized as a WWF Global 200 ecoregion and spans three Ramsar sites and two World Heritage Sites.</p>

          <p class='credits'><strong>Credits:</strong> Wikramanayake, E., M. McNight, E. Dinerstein, A. Joshi, B. Gurung, D. Smith. 2004. Designing a Conservation Landscape for Tigers in Human-Dominated Environments. Conservation Biology (18):839-844.</p>

          <p class='read_more hidden'><em><a href='http://data.globalforestwatch.org/datasets/04d892c083f54c638228931da081467b_4?filterByExtent=true' target='_blank'>Learn more or download data at Conservation International</a></em></p>
        </div>
      </div>
    </li>
    <li id='verified_carbon' class='source-item last'>
      <div class='source_header'>
        <strong class='source_title'>Verified Carbon Standard project boundaries</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='http://www.vcsprojectdatabase.org/' target='_blank' title='Learn more or request data at Alliance for Zero Extinction'>Learn more or request data at VCS Project Database<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays the location of VCS projects categorized as Reduced Emissions from Deforestation and Forest Degradation (REDD), Improved Forest Management (IFM) and Afforestation, Reforestation and Revegetation (ARR), which are managed to protect or increase forest carbon stocks</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>The VCS Project Database</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>New VCS REDD+ projects will be added as they become registered under the VCS</dd>
          </dl>
          <dl class='sources_row even'>
            <dt>Date of content</dt>
            <dd>Varies by project area and is specified in the records of each individual VCS project</dd>
          </dl>
        </div>

        <div class='source_summary'>
        <h3 class='overview_title'>Overview</h3>
          <p>The <a href="http://www.v-c-s.org">Verified Carbon Standard</a> (VCS) is the world’s most widely used voluntary greenhouse gas (GHG) emission reduction program. VCS projects are developed across a wide range of sectoral scopes, including those classified within the Agriculture, Forestry and Other Land Use (AFOLU) sector. These projects reduce emissions from forest-related activities around the world and apply innovative GHG accounting methodologies to quantify such emission reductions, which are independently verified and transparently registered.</p>
          <p>This layer shows VCS projects categorized as REDD (Reduced Emissions from Deforestation and Forest Degradation), IFM (Improved Forest Management) and ARR (Afforestation, Reforestation and Revegetation).  More detail on the VCS program and these project categories can be found in the <a href="http://www.v-c-s.org/program-documents">VCS Standard and VCS AFOLU Requirements</a> documents.</p>
          <p>In addition to these project activities, more than a dozen national and subnational jurisdictions around the world are applying the <a href="http://www.v-c-s.org/JNR">VCS Jurisdictional and Nested REDD+</a> framework to account for the emission reductions generated by their REDD+ policies and measures. This information will be added in the future as these programs register under the VCS.</p>

           <p class='credits'><strong>Citation:</strong>The VCS Project Database. 2015. <a href="http://www.vcsprojectdatabase.org">www.vcsprojectdatabase.org</a>.</p>


          <p class='read_more hidden'><em><a href='http://www.vcsprojectdatabase.org/' target='_blank'>Learn more or request data at VCS Project Database</a></em></p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id='people' class='people source-article'>
  <h2 class='source_category_title'>People</h2>
  <p class='source_category_description'>Data compiled under “People” indicate areas over which indigenous peoples or local communities have rights over land and/or certain resources.</p>

  <p class='source_category_description'>The laws of many countries separate land rights from resource rights. Legally recognized land rights extend to the land and certain resources associated with the land, depending on the nature of the right. The right to the land and certain resources include some combination of rights of access, use, management, exclusion, and alienation. Similarly, legally recognized resource rights can cover some combination of the right to access, use, manage, exclude, or alienate forests, wildlife, or other resources. Whether rights to the land or resources, the law may recognize their rights in perpetuity or for a limited period of time. </p>

  <p class='source_category_description'>Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.</p>

  <ul class='sources'>
    <li id='land_rights' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Land Rights</strong>
        <i class='expand_arrow'></i>
      </div>
      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas over which indigenous peoples or local communities enjoy rights to the land and certain resources</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Canada, Brazil and Panama</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of sources, including government agencies, NGOs and other organizations. For information on country-specific concessions data please refer to the Data page.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.</p>
            </dd>
          </dl>
        </div>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Land Rights” refers to areas over which indigenous peoples or local communities enjoy rights to the land and certain resources, whether legally recognized or not. The exact nature of these land rights varies among tenure type and country.</p>

          <p>The land rights data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>
          <p>Land rights data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations.</p>
          <p>See the <%= link_to 'Data page', "#{sources_path}#people" %> for details on specific data sets.</p>
          <p>If you are aware of land rights data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Land Rights.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently includes land rights data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_people_land_right">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_header_people_land_right'>
              <ul>
                <li><a href='#' data-slug="brasil_indigenous_lands">Brazil Indigenous Lands</a></li>
                <li><a href='#' data-slug="canada_aboriginal_lands">Canada Aboriginal Lands</a></li>
                <li><a href='#' data-slug="panama_comarcas">Panama Comarcas</a></li>
              </ul>
            </div>

            <div id="brasil_indigenous_lands" class="source_dropdown_body">
              <p class='download'><a href='http://www.funai.gov.br/index.php/servicos/geoprocessamento'>Download</a></p>

              <p>This data set displays the boundaries of areas designated as Indigenous Lands in Brazil. Indigenous Lands legally recognize indigenous peoples’ perpetual rights of access, use, withdrawal, management, and exclusion over the land and associated resources. Alienation of the land is prohibited. However, commercial use of forest resources is permitted, but cutting trees for sale requires approval by the National Legislature. Rights to subsoil resources may be obtained only with the approval of the National Legislature and after consultation with the affected indigenous peoples. This data set includes Indigenous Lands that are officially registered and those at various stages of the registration process.</p>

              <p class='credits'><strong>Credit:</strong>Fundação Nacional do Índio (FUNAI)</p>
            </div>

            <div id="canada_aboriginal_lands" class="source_dropdown_body">
              <p class='download'><a href='http://www.geobase.ca/geobase/en/data/admin/alta/description.html'>Download</a></p>

              <p>The Aboriginal Lands data set depicts the administrative boundaries (exterior limits) of lands where the title has been vested in specific Aboriginal Groups of Canada or lands which were set aside for their exclusive benefit. The Aboriginal Lands data set includes, but is not limited to, Indian Reserves,Cree-Naskapi Category 1A and 1A-N Lands, Yukon First Nation Settlement Lands, Kanesatake Mohawk Interim Land Base, the Inuit Owned Lands, Tlicho Lands, Inuvialuit Lands, Gwich’in Lands and Sahtu Lands.</p>

              <p class='credits'><strong>Credit:</strong>Government of Canada, Natural Resources Canada, Earth Sciences Sector, Geomatics Canada, Surveyor General Branch. Available through the <a href="http://data.gc.ca/eng/open-government-licence-canada">Open Government License - Canada</a></p>
            </div>

            <div id="panama_comarcas" class="source_dropdown_body">
              <p class='download'>Not currently available for download</p>
              <p>This data set displays the boundaries of areas designated as comarcas in Panama. Comarcas are legally recognized semi-autonomous areas where indigenous peoples own the land and resources with rights of access, use, withdrawal, management, and exclusion. Although the Government retains ownership of subsoil resources, the indigenous community must be consulted by government and private organizations for proposed developments on their lands. The government and mining concessionaire are required to guarantee benefits to the community and compliance with sustainable development practices.</p>

              <p class='credits'><strong>Credit:</strong>National Coordinating Body of Indigenous Peoples in Panama (COONAPIP)</p>
            </div>
          </div>
        </div>
      </div>
    </li>

    <li id='resource_rights' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Resource Rights</strong>
        <i class='expand_arrow'></i>
      </div>
      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays boundaries of areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Currently available for Cameroon, Equatorial Guinea, Liberia and Namibia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Generally based on a combination of sources, including government agencies, NGOs and other organizations. For information on country-specific concessions data please refer to the Data page.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Variable, depending on government agencies in each country and other data providers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies by country</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Some data sets displayed on Global Forest Watch include land and resource rights governed by customary tenure systems but that are not recognized by national laws.</p>
            </dd>
          </dl>
        </div>
      </div>
      <div class='source_body'>
        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“Resource Rights” refers to areas over which indigenous peoples or local communities enjoy rights to certain resources and a limited right to access the land, whether legally recognized or not, in order to exercise their resource rights. The exact nature of these resource rights varies among tenure type and country.</p>

          <p>The resource rights data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.</p>

          <p>Resource rights data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations. See the <%= link_to 'Data page', "#{sources_path}/people#resource_rights" %> for details on specific data sets.</p>

          <p>If you are aware of resource rights data for additional countries, please email us <a href='mailto:gfw@wri.org'>here</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “Resource Rights.” World Resources Institute. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em><%= link_to 'Learn more or download data', "#{sources_path}/people#resource_rights" %></em></p>
        </div>

        <div class='source_extended'>
          <p class='first'>GFW currently includes resource rights data for the following countries:</p>

          <div class="source_dropdown">
            <a href="#" class="source_dropdown_header source_dropdown_header_people_right">
              <h3 class='overview_title' title="Select a country"><span>Select a country</span> <i class='dropdown_arrow'></i></h3>
            </a>

            <div class='source_dropdown_menu source_dropdown_menu_header_people_right'>
              <ul>
                <li><a href='#' data-slug="cameroon_community_forest">Cameroon Community Forest</a></li>
                <li><a href='#' data-slug="equatorial_guinea_community_forests">Equatorial Guinea Community Forests</a></li>
                <li><a href='#' data-slug="liberia_community_forests">Liberia Community Forests</a></li>
                <li><a href='#' data-slug="namibia_community_forests">Namibia Community Forests</a></li>
              </ul>
            </div>

            <div id="cameroon_community_forest" class="source_dropdown_body">
              <p class='download'><a href='http://www.wri.org/our-work/project/congo-basin-forests/cameroon#project-tabs'>Download</a></p>
              <p>This data set displays the boundaries of areas designated as Community Forests in Cameroon. Community Forests legally recognize a community’s ownership rights to forest resources, both timber and non-timber. It includes the right to access, use, withdraw for commercial purposes or subsistence, and exclude others from the forest. The land remains owned by the Cameroonian Government. The community’s rights to forest resources are renewed every five years as long as the community complies with the Community Forest Management Agreement. A community may also contract with a third party to commercially harvest timber.</p>

              <p class='credits'><strong>Credit:</strong> WRI Congo Basin Forest Atlas</p>
            </div>
            <div id="equatorial_guinea_community_forests" class="source_dropdown_body">
              <p class='download'><a href="http://www.wri.org/resources/maps/forest-atlas-equatorial-guinea">Download</a></p>

              <p>This data set displays the boundaries of areas designated as Community Forests in Equatorial Guinea. Community Forests legally recognize a community’s right to access government owned land in order to use the forest for subsistence. The forest remains owned by the government, and must be adjacent to the community. Community Forests are of perpetual duration.</p>
              <p class='credits'><strong>Credit:</strong> WRI Congo Basin Forest Atlas</p>
            </div>

            <div id="liberia_community_forests" class="source_dropdown_body">
              <p class='download'>Currently unavailable for download</p>
              <p>Communal Forests are areas set aside by statute or regulation for the sustainable use of forest products by local communities or tribes on a non-commercial basis. According to the National Forestry Reform Law of 2006, no prospecting, mining, settlement, farming or commercial timber extraction is permitted on community forests.</p>
              <p class='credits'><strong>Credit: USAID-Liberia PROSPER</strong>-</p>
            </div>

            <div id="namibia_community_forests" class="source_dropdown_body">
              <p class='download'><a href="http://www.nacso.org.na/coninfo.php">Download</a></p>
              <p>This data set displays the boundaries of areas registered as Community Forests in Namibia. Community Forests are recognized by the Minister of Environment and Tourism as communal lands subject to a management plan agreed upon by the Minister and a representative body of communal land members. In accordance with the agreement, the management plan grants communal land members the rights to manage and use natural resources, including the removal of forest produce for fuel, personal shelter, or livestock shelter; allows for agricultural activity; allows communal land members to authorize others to use the community forest’s natural resources; and allows communal land members to collect a fee and set conditions for the use of natural resources.</p>
              <p class='credits'><strong>Credit:</strong>Namibian Association of Community Based Natural Resource Management (CBNRM) Support Organisations (NACSO)</p>
            </div>
          </div>
        </div>
      </div>
    </li>

    <li id='grump2000' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Population Density</strong>
        <i class='expand_arrow'></i>
      </div>
      <div class='source_body'>
        <div class='source_download'>
          <a href='http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-density/data-download' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Measures human population per square kilometer</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Square Km Grid (30 Arc Seconds)</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href="http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-density" target="_blank">Columbia University Center for International Earth Science Information Network (CIESIN)</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>None planned</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2000</dd>
          </dl>
         </div>
      </div>
      <div class='source_body'>
        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>
          <p>“The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 in a 1 kilometer global grid. The population density grids measure population per square kilometer.</p>

          <p>A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values to grid cells. The population count grids are divided by the land area grid to produce population densities. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), the World Bank, and Centro Internacional de Agricultura Tropical (CIAT).</p>

          <p class='credits'><strong>Citation:</strong> Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) D.L. Balk, U. Deichmann, G. Yetman, F. Pozzi, S.I. Hay and A. Nelson. Determining Global Population Distribution: Methods, Applications and Data., Advances in Parasitology 62: 119-156, 2006. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

        </div>
      </div>
    </li>
  </ul>
</article>

<article id='stories' class='stories source-article'>
  <h2 class='source_category_title'>Stories</h2>
  <p class='source_category_description'>Stories represent qualitative or anecdotal data on forests, submitted by users or written and compiled from other sources.</p>

  <ul class='sources'>
    <li id='user_stories' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>User Stories</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p>This layer displays forest-related stories reported by GFW users. Stories are tagged to a specific location and can include photos, video, or explanatory text. See all GFW stories or report your own <%= link_to 'here', stories_path %>.</p>
        </div>
      </div>
    </li>

    <li id='mongabay' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Mongabay Stories</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p>This layer displays stories from <a href='http://www.mongabay.com/' target='_blank'>Mongabay.com</a>, a leading environmental science and conservation news website. Stories published within one year of the current date are tagged to a specific location and provide in-depth research or commentary on local forest issues. More information can be found at <a href='http://www.mongabay.com/' target='_blank'>Mongabay.com</a></p>
        </div>
      </div>
    </li>
    <li id='infoamazonia' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Earth Journalism Network Stories</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p>This layer displays stories sourced from the <a href="http://earthjournalism.net/">Earth Journalism Network</a>, a project of Internews that empowers and enables journalists from developing countries to cover the environment more effectively. Earth Journalism Network provides geo-tagged, syndicated stories through regional platforms, including InfoAmazonia (the Amazon) and Ekuatorial (Indonesia). Stories are tagged to a specific location and provide in-depth research or commentary on local forest issues. More information can be found at <a href="http://earthjournalism.net/">Earth Journalism</a>.</p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id='base_maps' class="source-article">
  <h2 class='source_category_title'>Base maps</h2>
  <p class='source_category_description'>Base maps provide a variety of map backgrounds for visual comparison with other data.</p>

  <ul class='sources'>
    <li id='grayscale' class='source-item first'>
      <div class='source_header'>
        <strong class='source_title'>Default</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class='first'>The default base map is derived from Google Maps and shows political boundaries, major geological features, and other key areas of interest. Read the terms of service <a href='http://www.google.com/help/terms_maps.html' target='_blank'>here</a>.</p>
        </div>
      </div>
    </li>

    <li id='terrain' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Terrain</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class='first'>Google Maps’ terrain base map shows geographical and topographical details. Scale varies by location. Read the terms of service <a href='http://www.google.com/help/terms_maps.html' target='_blank'>here</a>.</p>
        </div>
      </div>
    </li>

    <li id='satellite' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Satellite</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class='first'>Google Maps’ satellite base map consists of a mix of recent (1–3 years old) mid-resolution and high-resolution satellite and aerial imagery from multiple providers for a given area. TerraMetrics TruEarth 15-meter imagery is the baselayer imagery that covers the entire globe, and Google adds high-resolution imagery, where available, over TruEarth 15-meter imagery to provide additional visual details. Read the terms of service <a href='http://www.google.com/help/terms_maps.html' target='_blank'>here</a>.</p>
        </div>
      </div>
    </li>

    <li id='roads' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Roads</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class="first">Google Maps Road Network base map shows the extent of collected and generated road features. The map is proprietary to Google and cannot be downloaded. Scale varies by location. Read the terms of service <a href="http://www.google.com/help/terms_maps.html" target="_blank">here</a>.</p>
        </div>
      </div>
    </li>

    <li id='treeheight' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Tree Height</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class='first'>This base map depicts the highest points in the forest canopy. Its spatial resolution is 0.6 miles (1 km) and was validated against data from a network of nearly 70 ground sites around the world. It was developed by a team of scientists from NASA’s Jet Propulsion Laboratory, Pasadena, California; the University of Maryland, College Park; and Woods Hole Research Center, Falmouth, Massachusetts. The map was created using 2.5 million carefully screened, globally distributed laser pulse measurements from space. The light detection and ranging (Lidar) data were collected in 2005 by the Geoscience Laser Altimeter System instrument on NASA’s Ice, Cloud, and land Elevation Satellite (ICESat).</p>

          <p class='credits'><strong>Source:</strong> <a href='http://www.nasa.gov/topics/earth/features/forest20120217.html' target='_blank'>www.nasa.gov/topics/earth/features/forest20120217.html</a></p>
        </div>
      </div>
    </li>

    <li id='landsat' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Landsat Composites</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_summary'>
          <p class='first'>These base maps show a composite of the best annual Landsat satellite imagery (USGS/NASA) from 1999 to 2012. The annual composites were generated by Google in the Google Earth Engine. The Landsat composites display “Top-of-Atmosphere,” or the most cloud-free, images at 30-meter resolution. More information on Landsat imagery is available from the <a href='http://landsat.usgs.gov/' target='_blank'>Landsat website</a>. Read the terms of service <a href='http://www.google.com/help/terms_maps.html' target='_blank'>here</a>.</p>
          <p><strong>Note:</strong> Because of the very large file size of the annual Landsat composite layers, images will take some time to reload on the website and as you zoom in and out.</p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id='countries' class="source-article">
  <h2 class='source_category_title'>Countries</h2>
  <div class='single-source-item'>
    <p>Data provided on the <%= link_to 'country pages', countries_path %> come from a range of sources. Read below to learn more about each.</p>

    <h3 class='overview_title'>Food and Agriculture Organization of the United Nations (FAO)</h3>
    <p>Many figures on the Country Profiles come from the FAO’s Global Forest Resources Assessment (FRA). As stated on the <a href='http://www.fao.org/forestry/fra/fra2010/en/' target='_blank'>FAO website</a>, the FRA is a comprehensive assessment of forests and forestry that examines the current status and recent trends for different variables covering the extent, condition, uses and values of forests. To create the report, information was collated from 233 countries and territories for four points in time: 1990, 2000, 2005 and 2010. FAO worked closely with countries and specialists in the design and implementation of FRA 2010 - through regular contact, expert consultations, training for national correspondents and ten regional and subregional workshops. More than 900 contributors were involved, including 178 officially nominated national correspondents and their teams. To learn more, visit the <a href='http://www.fao.org/forestry/fra/fra2010/en/' target='_blank'>FAO website</a> or <a href='http://www.fao.org/docrep/017/i3110e/i3110e.pdf' target='_blank'>download the full report</a>.</p>

    <p>The Country Profiles also present data from <a href='http://faostat.fao.org/' target='_blank'>FAOSTAT</a>, FAO’s platform for data collection and dissemination. According to FAO, the FAOSTAT <a href='http://faostat3.fao.org/faostat-gateway/go/to/download/G2/*/E' target='_blank'>Emissions Land Use database</a> provides country-level estimates of greenhouse gas (GHG) emissions based on FAOSTAT activity data using Tier 1 computations, following 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National GHG Inventories. Changes in carbon stocks and ecosystem function linked to anthropogenic activities such as land-use change and land management determine emissions and removals of GHG that are reported by countries for the IPCC Land Use, Land-Use Change and Forestry (LULUCF) categories. To learn more, visit the <a href='http://faostat.fao.org/' target='_blank'>FAOSTAT website</a>.</p>

    <p>Data on the economic value and employment in national forestry sectors are reported from “Contribution of the forestry sector to national economies, 1990-2006” by A. Lebedys. Forest Finance Working Paper FSFM/ACC/08. FAO, Rome. To learn more, <a href='ftp://ftp.fao.org/docrep/fao/011/k4588e/k4588e00.pdf' target='_blank'>download the full report</a>.</p>
    <p><strong>Figures:</strong> type of forest, economic value of the forestry sector, employment, carbon stocks, emissions</p>

    <h3 class='overview_title'>University of Maryland (UMD)/Google</h3>
    <p>Figures for tree cover and tree cover loss and gain presented on the country and global overview pages were calculated using tabular data from a 2013 publication, “<a href='http://www.sciencemag.org/content/342/6160/850' target='_blank'>High-Resolution Global Maps of 21st-Century Forest Cover Change</a>" by Hansen <em>et al</em>.</p>

    <p>To learn more, visit the <%= link_to 'Forest Change tab', "#{sources_path}#forest_change" %> on the Data page.</p>

    <p><strong>Figures:</strong> Hansen/UMD/Google/USGS/NASA circle graphs, 2000 tree cover, all global overview graphs and rankings</p>

    <h3 class='overview_title'>European Space Agency (GlobCover)</h3>
    <p>Data from the European Space Agency’s GlobCover was used to visualize tree cover on the Country Profiles. As stated on the <a href='http://due.esrin.esa.int/globcover/' target='_blank'>ESA website</a>, GlobCover is an initiative which began in 2005 in partnership with a number of organizations. The aim of the project was to develop a service capable of delivering global composites and land cover maps using input observations from the 300m MERIS sensor on board the ENVISAT satellite mission. ESA makes available the land cover maps, which cover 2 periods: December 2004 - June 2006 and January - December 2009. To learn more, visit the <a href='http://due.esrin.esa.int/globcover/' target='_blank'>GlobCover website</a> or <a href='http://due.esrin.esa.int/globcover/LandCover2009/Globcover2009_V2.3_Global_.zip' target='_blank'>download the 2009 GlobCover map</a>.</p>

    <p><strong>Figures:</strong> Map of tree cover</p>

    <h3 class='overview_title'>The Rights and Resources Initiative (RRI)</h3>
    <p>Figures on forest tenure come from the Rights and Resource Initiative and are based on data adapted from the <a href='http://www.rightsandresources.org/documents/index.php?pubID=1075' target='_blank'>Tropical Forest Tenure Assessment</a>. The goal of the report is to present and analyze the state of forest tenure in much of the world’s tropical forests. As stated on <a href='http://www.rightsandresources.org/programs.php?id=237' target='_blank'>RRI's website</a>, the tenure systems represented by this data is sourced from governments, and therefore only reflects those systems of natural resource management that are legally recognized by those governments. Such officially outlined tenure systems fall under the category of statutory tenure regimes within these studies. The official data often presents an incomplete picture of the institutions that actually manage natural resources, particularly at a local level. To learn more, visit the <a href='http://www.rightsandresources.org/programs.php?id=237' target='_blank'>RRI forest tenure page</a>.</p>

    <p><strong>Figures:</strong> Forest tenure</p>
  </div>
</article>

<article id='code' class="source-article">
  <h2 class='source_category_title'>Code</h2>
  <div class='single-source-item'>
    <p>The Global Forest Watch website and Global Forest Watch API are both collaborative and open-source projects hosted on <a href='https://github.com/' target='_blank'>GitHub</a>. If you would like to contribute source code, please send pull requests to our <a href='https://github.com/Vizzuality/gfw' target='_blank'>website repository</a> or <a href='https://github.com/wri/gfw-api' target='_blank'>API repository</a>. All Global Forest Watch source code is released under <a href='http://opensource.org/licenses/MIT' target='_blank'>The MIT License (MIT)</a>.</p>
  </div>
</article>

<article id='earth_engine' class="source-article">
  <h2 class='source_category_title'>GFW on Google Earth Engine</h2>
  <div class='single-source-item'>
    <p>The following data sets on Global Forest Watch were created using <a href="http://earthengine.google.org" target="_blank">Google Earth Engine</a>, Google's geospatial analysis tool: the University of Maryland/Google's annual tree cover loss, tree cover gain, and tree cover data, and the Landsat base maps.</p>

    <p>Additionally, the tropical forest carbon stocks layer is being stored and served from <a href="http://mapsengine.google.com/" target="_blank">Google Maps Engine</a> and styled by Earth Engine, the Imazon SAD data are created using Imazon's algorithms on Earth Engine, and analyses of the University of Maryland/Google data, as well as computations of area, are being performed by Earth Engine.</p>

    <p><a href="http://earthengine.google.org/signup" target="_blank">Sign up here</a> to access the full suite of Earth Engine tools and data, including data available on Global Forest Watch, 40+ years of Landsat data, many additional satellite data sets, elevation data, atmospheric data, and data you upload yourself. For information about how to analyze Global Forest Watch data, please visit our <a href="https://sites.google.com/site/earthengineapidocs/global-forest-watch-tutorial" target="_blank">tutorial</a>.</p>
  </div>
</article>

<article id='arcgis' class="source-article">
  <h2 class='source_category_title'>GFW on ArcGIS Online</h2>
  <div class='single-source-item'>
    <p>View <a href='http://gfw.maps.arcgis.com' target='_blank'>Global Forest Watch’s ArcGIS Online page</a> to browse for downloadable data, maps, and spatial data resources. View our data, overlay our data sets with your own, or browse any of the available data layers available through ArcGIS Online. With a free <a href='http://www.esri.com/software/arcgis/arcgisonline/features/free-personal-account' target='_blank'>ArcGIS Online public account</a> you can create, store, and manage maps, apps, and data, and share them with others.</p>
  </div>
</article>



<article id='country_dialog' class="source-article">
  <div class='source_header'>
    <strong class='source_title'>Analyze forest change in a country</strong>
    <i class='expand_arrow'></i>
  </div>

  <div class='source_body'>
    <div class='source_summary'>
      <p>1. Select a country</p>
      <p>2. Select “analyze on map” from the right-hand menu to analyze forest change data within the country.</p>
      <p><%= image_tag('tutorial-screenshots/screenshot-4.png') %></p>
      <p>To analyze forest change data within a subnational jurisdiction, click “select subnational jurisdiction” located below the country name, and then select “analyze on map” from the right-hand menu.</p>
      <p><%= image_tag('tutorial-screenshots/screenshot-3.png') %></p>
      <p>3. Adjust the analysis by selecting different forest change data layers from the tab above the map and/or by changing the timeline located on the bottom of the map.</p>
      <p>Learn more about analyzing forest change data within a country or subnational jurisdiction on the <a href="/howto">How To page</a>.</p>
    </div>
  </div>
</article>

<article id="fires-data" class="source-article">
  <h2 class='source_category_title'>Fires</h2>
  <ul class='sources'>
    <li id='fires' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>FIRMS active fires</strong>
        <i class='expand_arrow'></i>
        <em class='source_description'>(daily, 1km, global, NASA)</em>
      </div>

      <div class='source_body'>
        <div class='source_download'>
          <a href='https://earthdata.nasa.gov/data/near-real-time-data/firms' target='_blank' title='Download at project website'>Download at project website<i class='arrow_down'></i></a>
        </div>

        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays fire alert data for the past 7 days</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1 × 1 kilometer</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd><a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>MODIS</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Frequency of updates</dt>
            <dd>Daily</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Past 7 days</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Not all fires are detected. There are several reasons why MODIS may not have detected a certain fire. The fire may have started and ended between satellite overpasses. The fire may have been too small or too cool to be detected in the (approximately) 1 square kilometer pixel. Cloud cover, heavy smoke, or tree canopy may completely obscure a fire.</p>
              <p>It is not recommended to use active fire locations to estimate burned area due to spatial and temporal sampling issues.</p>
              <p>When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>

          <p>The Fire Information for Resource Management System (FIRMS) delivers global MODIS-derived hotspots and fire locations. The active fire locations represent the center of a 1-kilometer pixel that is flagged by the MOD14/MYD14 Fire and Thermal Anomalies Algorithm as containing one or more fires within the pixel.</p>

          <p>The near real-time active fire locations are processed by the <a href='https://earthdata.nasa.gov/data/near-real-time-data' target='_blank'>NASA Land and Atmosphere Near Real-Time Capability for EOS (LANCE)</a> using the standard MODIS Fire and Thermal Anomalies product (MOD14/MYD14). Data older than the past 7 days can be obtained from the <a href='https://earthdata.nasa.gov/data/near-real-time-data/firms/active-fire-data#tab-content-6' target='_blank'>Archive Download Tool</a>. The tool provides near real-time data and, as it becomes available and is replaced with the standard NASA (MCD14ML) fire product.</p>

          <p>More information on active fire data is available from the <a href='https://earthdata.nasa.gov/data/near-real-time-data/firms' target='_blank'>NASA FIRMS website</a>.</p>

          <p class="credits"><strong>Citation:</strong>NASA FIRMS. “NASA Fire Information for Resource Management System (FIRMS).” Accessed on [date]. <a href="earthdata.nasa.gov/data/near-real-time-data/firms" target="_blank">earthdata.nasa.gov/data/near-real-time-data/firms</a>.</p>

          <p class='credits'><strong>Suggested citation for data as displayed on GFW:</strong> “NASA Active Fires.” NASA FIRMS. Accessed through Global Forest Watch on [date]. <a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>

          <p class='read_more hidden'><em>Learn more or download data at the <a href='https://earthdata.nasa.gov/data/near-real-time-data/firms' target='_blank'>NASA FIRMS website</a></em></p>
        </div>
      </div>
    </li>

    <li id='burn_scars' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Burn scars</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Provides an estimate of the extent of land burned by fire</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>30 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Sumatra, Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Landsat</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>May 1, 2014 to present</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>This data layer is provided as a beta analysis product and should be used for visual purposes only.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>

          <p>This data layer provides the extent of burn land area, or burn scars, mapped from Landsat satellite imagery, using Google Earth Engine. This analysis was conducted by the Data Lab team (Robin Kraft, Dan Hammer, and Aaron Steele) of the World Resources Institute using Google Earth Engine. This analysis will be updated regularly as additional Landsat imagery becomes available.</p>

          <p>This analysis was conducted as an open source project; code is available here: <a href="https://gist.github.com/robinkraft/077c14d35a50a8b31581" target="_blank">https://gist.github.com/robinkraft/077c14d35a50a8b31581</a></p>

          <p class="credits"><strong>Credit:</strong>Elvidge, Christopher D. and Kimberly Baugh. 2014. Burn scar mapping from Landsat 8. Presentation at APAN meeting in Bandung, Indonesia. January 20.</p>

          <p class='credits'><strong>URL:</strong><a href='http://www.apan.net/meetings/Bandung2014/Sessions/EM/Elvidge_L8_burnscar_20140120.pdf' target='_blank'>http://www.apan.net/meetings/Bandung2014/Sessions/EM/Elvidge_L8_burnscar_20140120.pdf</a>.</p>

        </div>
      </div>
    </li>

    <li id='noaa-18-fires' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>NOAA-18 FIRES</strong>
        <i class='expand_arrow'></i>
      </div>
      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies coordinate locations of fire hotspots</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1.1 x 1.1 kilometers</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Southeast Asia</dd></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>NOAA-18</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>October 22, 2014 to present</dd>
          </dl>
          <d1 class='sources_row'>
            <dt>Frequency of Updates</dt>
            <dd>Daily</dd>
          </d1>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Not all fires are detected. Fire hotspots can go undetected when an area is not covered in the NOAA-18 satellite pass, or under cloudy and overcast conditions. Limitations with the fire detection algorithm may also cause fires to go undetected.</p>
              <p>It is not recommended to use fire locations to estimate burned are due to spatial and temporal sampling issues. A detected fire does not necessarily mean that the entire area represented by the 1.1 km2 pixel is on fire.</p>
              <p>When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class='overview_title'>Overview</h3>

          <p>The Meteorological Service of Singapore derives coordinate locations for fire hotspots using satellite imagery provided by the National Oceanic and Atmospheric Administration satellite 18 (NOAA-18). The hotspot locations represent the center of a 1.1 km2 pixel that was determined to have fires in that pixel. The presence of fires is determined by a computer algorithm that interprets infrared satellite imagery to identify high temperature bodies on the ground such as land and forest fires.</p>

          <p>Data is archived from October 22, 2014 to present on Global Forest Watch, and is available for download in 30 day intervals.</p>

          <p class="credits"><strong>Citation:</strong>Suggested citation for data as displayed on GFW:“ NASA FIRMS.” Meteorological Service Singapore.  Accessed through Global Forest Watch on [date].<a href='http://www.globalforestwatch.org' target='_blank'>www.globalforestwatch.org</a>.</p>
        </div>
      </div>
    </li>
  </ul>
</article>



<article id="air-quality" class="source-article">
  <h2 class='source_category_title'>Air Quality</h2>
  <ul class='sources'>
    <li id='air-q' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Air Quality</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Provides real time air quality index values for cities in the Southeast Asia, where available.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Hourly readings</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global, where data are available</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Compiled global data provided by <a target="_blank" href="http://aqicn.org/map/world/">http://aqicn.org/map/world/</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Hourly</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Quality of data depends on the original source data and may vary. Air quality data are not currently available for Indonesia.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This data layer provides hourly Air Quality Index (AQI) for many cities in Southeast Asia and around the world. Where available, AQI values indicate the risk of pollutant levels to human health. Data for all cities are expressed as AQI regardless of the nationally used standard, to enable comparison across countries and regions. For more information on AQI data, please see <a target="_blank" href="http://www.airnow.gov/index.cfm?action=aqibasics.aqi">http://www.airnow.gov/index.cfm?action=aqibasics.aqi</a>.</p>
          <p>The AQICN website, originally created to share air quality data for China, compiles publicly available real-time hourly pollutant readings for cities around the globe, in their Air Pollution in Asia: Real-time Air Quality Index Visual Map, <a href="http://aqicn.org/map/" target="_blank">http://aqicn.org/map/</a>.</p>
          <p>Data sources:</p>
          <ul class="bullets">
            <li>Malaysia Hourly Air Quality Index (AQI) from the Malaysia Department of the Environment, <a target="_blank" href="http://apims.doe.gov.my/apims/hourly2.php">http://apims.doe.gov.my/apims/hourly2.php</a></li>
            <li>Singapore Hourly Pollutant Standards Index (PSI) values provided by the Singapore National Environment Agency (NEA) and converted to Air Quality Index for standardization in readings for all reporting countries. Original data provided at <a target="_blank" href="http://app2.nea.gov.sg/anti-pollution-radiation-protection/air-pollution-control/psi/psi">http://app2.nea.gov.sg/anti-pollution-radiation-protection/air-pollution-control/psi/psi</a></li>
            <li>Thailand AQI from <a href="http://www.aqmthai.com/aqi.php" target="_blank">http://www.aqmthai.com/aqi.php</a></li>
          </ul>
          <p class="credits"><strong>Credit:</strong> Data compiled by the website, Air Pollution in Asia, in their Real-time Air Quality Index Visual Map.<br><a target="_blank" href="http://aqicn.org/map/">http://aqicn.org/map/</a></p>
        </div>
      </div>
    </li>

    <li id='wind_direction' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Wind direction</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Provides an animation of wind direction data collected 4 times daily.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>NOAA</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>4 times a day</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p><a href="http://www.weather.gov/disclaimer" target="_blank">http://www.weather.gov/disclaimer</a></p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This visualization of wind direction in near-real time combines the visualization of wind direction data from the Global Forecast System (GFS) spectral model <a href="http://nomads.ncep.noaa.gov/txt_descriptions/GFS_high_resolution_doc.shtml" target="_blank">http://nomads.ncep.noaa.gov/txt_descriptions/GFS_high_resolution_doc.shtml</a> as well as code developed by Cameron Beccario <a href="https://github.com/cambecc/earth" target="_blank">https://github.com/cambecc/earth</a>, to create a visualization by interpolating wind direction between measurements. The GFW model was originally developed by J. Sela (Sela 1982, 1988), and is continuously improved by the Global Climate and Weather Modeling Branch which conducts a program of research and development in support of the Environmental Modeling Center (EMC) (<a href="http://www.emc.ncep.noaa.gov/" target="_blank">http://www.emc.ncep.noaa.gov/</a>) of the National Centers for Environmental Prediction (NCEP <a href="http://www.ncep.noaa.gov/">http://www.ncep.noaa.gov/</a>). The operational forecasting mission for global prediction for medium range (3-14 days) and for extended range (week2 – S/I) is at <a href="http://www.emc.ncep.noaa.gov/gmb/mission.html" target="_blank">http://www.emc.ncep.noaa.gov/gmb/mission.html</a>. The operational GFS consists of the final Global Data Assimilation System, the GFS forecasts (GFS), and the Ensemble forecasts (ENS). The GFS is a consolidation of the forecasts formerly known as the Aviation (AVN) and the Medium Range Forecast (MRF).</p>
          <p>The wind direction visualization utilizes data from the NOAA National Operational Model Archive &amp; Distribution System, which may be accessed at <a href="http://nomads.ncdc.noaa.gov/data.php?name=access#hires_weather_datasets">http://nomads.ncdc.noaa.gov/data.php?name=access#hires_weather_datasets</a>. Wind direction data are collected by satellite 4 times per day.</p>
          <p class="credits"><strong>Citation:</strong>NOAA Operational Model Archive and Distribution System<br><a href="http://nomads.ncdc.noaa.gov/data.php?name=access#hires_weather_datasets" target="_blank">http://nomads.ncdc.noaa.gov/data.php?name=access#hires_weather_datasets</a><br><a href="http://nomads.ncdc.noaa.gov/">Learn more or download data</a></p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id="imagery" class="source-article">
  <h2 class='source_category_title'>Imagery</h2>
  <ul class='sources'>
    <li id='digital-globe' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>DIGITAL GLOBE – FIRST LOOK IMAGERY</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Provides imagery of burn scars and active fires for ground truthing the NASA Active Fire alerts.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>50-60 cm GSD at-nadir, up to 1 m at 45˚ off-nadir</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>DigitalGlobe</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>

            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Effective emergency planning and response requires quick and easy access to accurate, up-to-date information. DigitalGlobe’s FirstLook is an online subscription service for emergency management that provides fast web-based access to pre-and post-event imagery of world disasters delivered to almost any desktop or web-based mapping platform. High-resolution satellite imagery provides the essential information required for emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery.</p>
        </div>
      </div>
    </li>

    <li id='landsat-8-pan-sharpened' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>LANDSAT 8 PAN-SHARPENED</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>FUNCTION Provides access to Landsat 8 OLI 30m natural color scenes enhanced with 15m Panchromatic data covering the landmass of the world for visual interpretation.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>15 x 15 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Global</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>NOAA</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Every 6 hours</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>ArcGIS Online subscription required.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Dynamic Image Service provides access to Landsat 8 OLI 30m natural color scenes enhanced with 15m Panchromatic data covering the landmass of the world for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. The service is updated on a daily basis to include the latest ‘Best’ scenes from the USGS.</p>
          <p>Each scene is processed on-the-fly from the original Landsat 8 12 bit/channel scenes (with only lossless compression applied) to compute apparent reflectance and then apply pansharpening from the 15m panchromatic band to the 30m multispectral bands. An appropriate renderer is then applied to return an 8 bit/channel image. A Dynamic Range Adjustment (DRA) version of the function is also included, which enables the full dynamic range of the images.</p>
          <p>Currently ‘Best’ is defined as the latest 4 scenes for each path/row with a cloud coverage of &lt; 50%, plus the scene closest to the corresponding GLS 2000 scene. Over time the older or cloudier scenes will be removed from the service.</p>
          <p>Each scene is attributed with properties such as the acquisition date and estimated cloud cover percentage. The service is time-enabled, so applications can restrict the displayed scenes to specific date range. Additionally filters can be set to restrict and order the scenes based on the attributes.</p>
          <p>Overviews are created at 300m resolution meaning that at scales smaller than approximately 1:1Million overviews based on the ‘best’ or ‘latest’ scenes will be displayed. To work with an individual scene(s) at all scales use the lock raster functionality - (Set display order to a list of images Web Maps). Note that ‘Lock Raster’ should not be used on the service except for short periods of time, since each day a new service is created the Object IDs will change.</p>
          <p>The Pan-sharepened imagery is a service provided through Esri’s ArcGIS Online subscription. <a href="http://www.nasa.gov/mission_pages/landsat/main/index.html">http://www.nasa.gov/mission_pages/landsat/main/index.html</a></p>
          <p class="credits"><strong>Credit:</strong> <a href="http://www.nasa.gov/mission_pages/landsat/main/index.html">Landsat 8</a> is a collaboration between NASA and the U.S. Geological Survey (USGS).</p>
        </div>
      </div>
    </li>
  </ul>
</article>

<article id="production-suitability" class="source-article">
  <h2 class='source_category_title'>Production suitability</h2>
  <ul class='sources'>
    <li id='custom-suitability-map' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>CUSTOM SUITABILITY MAP</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Identifies degraded land best suited for palm oil production.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>Varies depending on suitability criteria.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Southeast Asia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>See more information <a href="http://data.wri.org/POTICO/English_how_to_identify_degraded_land_for_sustainable_palm_oil_in_indonesia.pdf" target="_blank">here</a></dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies depending on suitability criteria</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>Protected area designations, such as “National Park,” can be applied differently in different countries. Therefore, the associated IUCN category and its description of protection may also vary by country.</p>
              <p>Protected areas with no boundary data are displayed as brown dotted boxes, which represent the reported protected area size. The box is centered around a single point location and the borders do not indicate the real boundary of the protected area.</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Palm oil production in Indonesia has the potential to generate local benefits if oil palm cultivation expansion follows sustainable planning and management practices, including respect for local interests and rights. Potential benefits include increased incomes, profits, and government revenues, reduced poverty, and improved natural resource management. Whether this potential is achieved will depend on how new areas for oil palm cultivation are identified.</p>
          <p>The suitability mapper method was designed in accordance with established standards for sustainable palm oil production, such as those of the roundtable on Sustainable Palm Oil (RSPO). The method incorporates relevant Indonesian laws and policies and is consistent with proposed national REDD+ strategies to support palm oil production on low carbon degraded land. The method consists of a desktop analysis using readily available data and rapid field assessments. It is based on a set of indicators related to selected environmental, economic, social, and legal considerations.</p>
          <p>This method can be used by companies as a first step in a site selection process for certified sustainable plantation and can inform government officials and nongovernmental organizations (NGOs) in assessing land use policy options to support the expansion of sustainable palm oil production on degraded land. However, since it is designed primarily to rapidly identify the highest priority areas for further investigation, it should not be used to predetermine where oil palm cultivation expansion should occur.</p>
          <p>This method is a first step in the site selection process, and can reduce the costs of implementing the additional due diligence activities required to confirm the suitability of a potential site for oil palm cultivation. In addition to identifying lands with the suitability mapper, other activities are necessary to ensure this due diligence, such as community mapping to document community claims and rights, conducting high conservation value (HCV) and social impact assessments, implementing a comprehensive free prior and informed consent (FPIC) process, and fulfilling legal requirements.</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/POTICO/English_how_to_identify_degraded_land_for_sustainable_palm_oil_in_indonesia.pdf" target="_blank">here</a></strong></p>
          <p class="credits"><strong>Citation:</strong> The suitability mapper is a first step in the site selection process. Other activities are necessary to ensure due diligence, such as community mapping to document claims and rights, conducting high conservation value (HCV) and social impact assessments, implementing a comprehensive free prior and informed consent (FPIC) process, and fulfilling legal requirements.</p>
        </div>
      </div>
    </li>
    <li id='wri-suitability-standard' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>WRI SUITABILITY STANDARD - OIL PALM</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Shows potentially suitable areas for sustainable oil palm cultivation, according to the default settings determined by Project POTICO.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Kalimantan, Papua, Sumatra, and Sulawesi</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Varies</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>Shows potentially suitable areas for sustainable oil palm cultivation, according to the default settings determined by Project POTICO. These default environmental and crop criteria settings are based on the methodology developed by WRI and Sekala, detailed in Gingold et al. (2012), available online at <a href="http://www.wri.org/publication/identifying-degraded-land-sustainable-palm-oil-indonesia" target="_blank">http://www.wri.org/publication/identifying-degraded-land-sustainable-palm-oil-indonesia</a>. Suitability calculated using the following default values:</p>
          <ul class="bullets">
            <li>Land cover - Suitable: grassland/shrub; plantations; agriculture; settlements/other uses. Not suitable: primary forest; secondary forest, wetland</li>
            <li>Peat depth – Suitable:0 cm. Not suitable: any value &gt; 0 cm</li>
            <li>Slope – Suitable: 0-30%. Not suitable : &gt;30%</li>
            <li>Conservation area buffer – Suitable: &gt;1000 m. Not suitable: &lt;1000m</li>
            <li>Water resource buffer – Suitable: &gt; 100 m. Not suitable: &lt;100 m</li>
            <li>Elevation – Suitable: 0-1000m. Not suitable: &gt;1000 m</li>
            <li>Rainfall – Suitable: 1250-6000mm/yr. Not suitable: &gt;6000mm/yr; &lt;1250mm/yr</li>
            <li>Soil drainage – Suitable: good; moderately good; excessive; poor. Not suitable: very excessive; very poor; stagnant</li>
            <li>Soil depth – Suitable: moderately deep (51-75 cm); deep (76-100 cm); very deep (101-150 cm); extremely deep (&gt;150 cm). Not suitable: very shallow (0-10 cm); moderately shallow (26-50 cm)</li>
            <li>Soil acidity (pH) – Suitable: excessively acid (&lt;4.0); extremely acid (4.0-4.5); very strongly acid (4.6-5.0); strongly acid (5.1-5.5); moderately acid (5.6-6.0); slightly acid (6.1-6.5); neutral (6.6-7.3). Not suitable: none.</li>
            <li>Soil type – Suitable: Inceptisol; Oxisol; Alfisol; Ultisol; Spodosol; Entisol. Not suitable: Histosol</li>
          </ul>
        </div>
      </div>
    </li>
    <li id='conservation-areas' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>CONSERVATION AREAS</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays areas classified as conservation areas by the Ministry of Forestry.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Ministry of Forestry (year unknown). Kawasan Hutan (Forest Estate) land use maps, General Direktorat of Planning, Ministry of Forestry; using the subsets for conservation areas including Hutan Lindung (Protection Forest) and Hutan Konservasi (Conservation Forest); downloaded from <a href="http://appgis.dephut.go.id/appgis/kml.aspx" target="_blank">http://appgis.dephut.go.id/appgis/kml.aspx</a> in March 2010, with maps for Riau and Central Kalimantan downloaded in March 2011. Processed and provided by Greenpeace. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>Year unknown</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the conservation areas in Kalimantan. Conservation areas include the following legal classifications defined by the Ministry of Forestry (1:250,000 scale):</p>
          <ul class="bullets">
            <li>marine national park</li>
            <li>national park</li>
            <li>national protected area</li>
            <li>nature reserves</li>
            <li>protected (from patch)</li>
            <li>protection forest</li>
          </ul>
          <p>Download the full methodology document <strong><a href="http://appgis.dephut.go.id/appgis/kml.aspx" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='water-resources' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>WATER RESOURCES</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays water resources, derived from rivers data.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>N/A</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Bakosurtanal, the Indonesian National Coordinating Agency for Surveys and Mapping. Data available in Minnemeyer et al. (2009). Interactive Atlas of Indonesia's Forests CD-ROM. Washington, DC: World Resources Institute. Prepareed by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2009</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This data displays water resources as derived from rivers data, originally produced by Bakosurtanal, the Indonesian National Coordinating Agency for Surveys and Mapping. It was prepared by WRI for use in the Interactive Atlas of Indonesia's Forests (2009).</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/GFWcommodities/OP_suitability_water_buffer.zip" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='production-elevation' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Elevation</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays elevation.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>90 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Shuttle Radar Topography Mission (2008). Original data available at <a href="http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html" target="_blank">http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html</a>. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2008</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the elevation (in meters) for Kalimantan. 90 meter resolution. Data was prepared by the World Resources Institute for use in the Suitability Mapper (2012).</p>
          <p>Download the full methodology document <strong><a href="http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='production-slope' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SLOPE</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays slope.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>90 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Shuttle Radar Topography Mission (2008). Original data available at <a href="http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html" target="_blank">http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html</a>. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>2008</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the slope (in percent) of Kalimantan calculated from the elevation layer (90 meter resolution): ArcToolBox, Spatial Analyst Tools, Surface, Slope (Output measurement: percent_rise).Data was prepared by the World Resources Institute for use in the Suitability Mapper (2012).</p>
          <p>Download the full methodology document <strong><a href="http://www2.jpl.nasa.gov/srtm/cbanddataproducts.html" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='production-rainfall' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>Rainfall</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays rainfall for the period 1989-2009.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1000 meters</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Indonesia</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>WorldClim Global Climate data (1989–2009, 1000 m resolution). Data available at <a href="http://www.worldclim.org/" target="_blank">http://www.worldclim.org/</a>. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1989 - 2009</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the mean rainfall (in mm) for the period from 1989 to 2009 (1000 m resolution). Data was prepared by the World Resources Institute for use in the Suitability Mapper (2012).</p>
          <p>Download the full methodology document <strong><a href="http://www.worldclim.org/" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='production-soil-drainage' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SOIL DRAINAGE</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays soil drainage classified by category.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Kalimantan, Papua, Sumatra, and Sulawesi</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Regional Physical Planning Program for Transmigration (RePPProT) (1990). Data provided and processed by Daemeter Consulting. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1990</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows the soil drainage, based on result of a classification established from Kalimantan RePPProT dataon 'SL_drain1' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting and was prepared by the World Resources Institute for use in the Suitability Mapper (2012). Data separated into categories: stagnant; very poor; poor; moderately good; good; excessive; very excessive.</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/GFWcommodities/OP_suitability_soil_drainage.zip" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>

    <li id='production-soil-depth' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SOIL DEPTH</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays soil depth category by depth.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Kalimantan, Papua, Sumatra, and Sulawesi</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Regional Physical Planning Program for Transmigration (RePPProT) (1990). Data provided and processed by Daemeter Consulting. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1990</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows soil depth, bsaed on the result of a classification established from Kalimantan RePPProT data on 'Depthmnr1' field (1990, 1:250,000 scale). This data was provided and processed by Daemeter Consulting and was prepared by the World Resources Institute for use in the Suitability Mapper (2012). Data separated into categories: very shallow (0-10 cm); moderately shallow (26-50 cm); moderately deep (51-75 cm); deep (76-100 cm); very deep (101-150 cm); extremely deep (&gt;150 cm).</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/GFWcommodities/OP_suitability_soil_depth.zip" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>


    <li id='production-soil-acidity' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SOIL acidity</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays soil acidity.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Kalimantan, Papua, Sumatra, and Sulawesi</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Regional Physical Planning Program for Transmigration (RePPProT) (1990). Data provided and processed by Daemeter Consulting. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1990</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows soil acidity, based on Kalimantan RePPProT data (1990, 1:250,000 scale). This data was provided and processed by Daemeter Consulting and was prepared by the World Resources Institute for use in the Suitability Mapper (2012). Data separated into categories of acidity: excessively acid (&lt;4.0); extremely acid (4.0-4.5); very strongly acid (4.6-5.0); strongly acid (5.1-5.5); moderately acid (5.6-6.0); slightly acid (6.1-6.5); neutral (6.6-7.3).</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/GFWcommodities/OP_suitability_soil_acidity.zip" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>


    <li id='production-soil-type' class='source-item'>
      <div class='source_header'>
        <strong class='source_title'>SOIL type</strong>
        <i class='expand_arrow'></i>
      </div>

      <div class='source_body'>
        <div class='source_table'>
          <dl class='sources_row'>
            <dt>Function</dt>
            <dd>Displays soil type.</dd>
          </dl>
          <dl class='sources_row'>
            <dt>RESOLUTION / SCALE</dt>
            <dd>1:250,000</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Geographic coverage</dt>
            <dd>Kalimantan, Papua, Sumatra, and Sulawesi</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Source data</dt>
            <dd>Regional Physical Planning Program for Transmigration (RePPProT) (1990). Data provided and processed by Daemeter Consulting. Prepared by the World Resources Institute (2012).</dd>
          </dl>
          <dl class='sources_row'>
            <dt>FREQUENCY OF UPDATES</dt>
            <dd>None</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Date of content</dt>
            <dd>1990</dd>
          </dl>
          <dl class='sources_row'>
            <dt>Cautions</dt>
            <dd>
              <p>N/A</p>
            </dd>
          </dl>
        </div>

        <div class='source_summary'>
          <h3 class="overview_title">Overview</h3>
          <p>This layer shows soil type, based on the result of a classification established from Kalimantan RePPProT data on 'SL_ORDER' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting. Soil categories from RePPProT were then re-classified by the World Resources Institute according to the FAO Digital Soil Map of the World, for use in the Suitability Mapper (2012). The FAO data is available at <a href="http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116" target="_blank">http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116</a> . Data separated into categories: Inceptisol; Oxisol; Alfisol; Ultisol; Spodosol; Entisol; Histosol.</p>
          <p>Download the full methodology document <strong><a href="http://data.wri.org/GFWcommodities/OP_suitability_soil_type.zip" target="_blank">here</a></strong></p>
        </div>
      </div>
    </li>



  </ul>
</article>



